Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 April 2018 doi:10.20944/preprints201804.0302.v1

Colors of Spectrum: A Single Paradigm Explains the Heterogeneity in Disorder

Andras´ Lorincz˝

Faculty of Informatics Eotv¨ os¨ Lorand´ University Budapest Hungary

ABSTRACT can slow learning down. We embed our thoughts into a predictive autoencoding, goal-oriented model of a We propose a theory of ASD as a condition of comor- deterministic world. We compare this model to oth- bid cognitive impairments that corrupt the learning, ers, such as the noisy model, the Bayesian prior encoding, and manipulation of episodic and semantic theory, the mirror theory and the weak cen- memories. We consider (i) episodic and semantic mem- tral coherence theory. We argue that the predictive ory functions of the entorhinal-hippocampal complex, autoencoder model of the deterministic world harmo- (ii) constraints on the transfer and encoding of these nizes with these other models and embraces them in a memory components into neocortical areas, and (iii) straightforward way. the demands of cognitively manipulating memories in distributed computations being necessary for goal- AUTHOR SUMMARY oriented interactions. In ASD, learning and cognitive challenges manifest in diverse ways but especially in The core of our arguments is based on the autoen- high-complexity-model predictive control tasks with la- coder model of the entorhinal-hippocampal complex tent variables. ASD impairments in social interactions and its role in forming declarative memories. We represent a prototypical example. Social interactions argue that social behavior and language usage are are at the high end of complexity and require pro- highly complex cognitive tasks involving the encoding, cesses (i) – (iii) to work in a concerted fashion due learning, and manipulation of declarative memories to the need for the learning and estimation of many, with latent variables. All these processes can be cor- sometimes latent, parameters, including emotions, in- rupted in many ways. Corruptions may manifest as tention, physical and mental capabilities as well as the , , deficit, and depres- predictive modeling of these parameters for decision sion, among other disorders. However, if these symp- making and timed-action series. We put forth the idea toms are weak but co-occur, then the most strongly that autism is a result of an arbitrary combination of affected behavioral feature is the ability to solve com- otherwise not prominent corruptions in processes (i)- plex cognitive tasks. This condition can be brought (iii). Together, these corruptions may severely impair about by diverse combinations of impairments that intelligence and slow down learning, especially in high- sum up in corrupted social behavior the most. complexity learning tasks. Over time, slow learning may spare the spontaneous learning-by-doing method KEYWORDS - namely, repetitive behavioral patterns, whereas be- havioral failures related to complex tasks can restrict autism, , components, psychiatric impair- interest in such task, thus inducing a fear of novelty; ments, comorbidity conversely, the fear of novelty restricts interest and

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Colors of Autism Spectrum:

Contents

1 Introduction 3

2 Theoretical background in the context of experimental findings 4 2.1 Components: an overview of the concept ...... 4 2.2 Making sense with autoencoders ...... 5 2.3 The neural system of component learning ...... 6 2.3.1 The discretization of the allothetic space component ...... 6 2.3.2 The loop ...... 6 2.3.3 Episodic memory from temporal relations ...... 6 2.3.4 Forming semantic memory by factoring out time ...... 7 2.4 and component binding ...... 7 2.5 Two-stage operation: Memory encoding and consolidation ...... 8 2.5.1 Brain waves contribute to memory consolidation in many ways ...... 8 2.5.2 Consolidation of emotional components ...... 9 2.6 Bottom-up and top-down processing serves component formation ...... 9 2.7 Sparse representations ...... 9 2.7.1 Theoretical considerations regarding sparsity ...... 9 2.7.2 Constraints and errors in sparse representations ...... 10 2.7.3 Autoencoding and interpretability ...... 10 2.7.4 Impairments in the neuronal networks ...... 11 2.7.5 Brain structure impairments ...... 11 2.7.6 Differences in axial diffusivity, myelination, and synaptic homeostasis ...... 11 2.8 Summary of some phenomena that may impair component learning and cognitive manipulations . 12

3 ‘Results’: Falsifying issues 13 3.1 Comorbid disorders ...... 13 3.1.1 Epilepsy ...... 13 3.1.2 Schizophrenia ...... 15 3.1.3 Attention deficit, hyperactivity disorder ...... 16 3.1.4 Synesthesia ...... 17 3.2 Environmental effects: Discordant monozygotic twins ...... 17 3.3 Implicit learning, control, and procedural learning ...... 17 3.4 Slowed learning of components ...... 18 3.5 The self as a component ...... 19

4 Discussion: Comparisons with other models 19 4.1 ASD models: Strengths and weaknesses ...... 20 4.1.1 Noisy brain and excitation/inhibition ratio imbalance ...... 20 4.1.2 Bayesian Prior Theory ...... 21 4.1.3 Mirror Neuron Theory ...... 21 4.1.4 Weak Central Coherence Theory ...... 22 4.1.5 Going deeper: Specific models ...... 22 4.1.6 Delayed development in multisensory processing ...... 22 4.1.7 Role of inflammation ...... 23 4.1.8 The minicolumn hypothesis ...... 23 4.1.9 Cortical hyperexpansion, brain volume overgrowth, and spatio-temporal constraints . . . . 23 4.1.10 Malfunctioning ...... 24 4.1.11 Cognitive dysfunction ...... 24 4.1.12 Problems in the connectome ...... 25 4.2 Potential side effects of impairments of component formation ...... 25

5 Conclusions 27

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1 Introduction brain must engage in a disambiguating process to se- lect the best interpretation according to the available Below, we consider some arguments to formulate our spatio-temporal context and act accordingly. Thus, the single paradigm hypothesis on the origin of autism. model should be predictive. However, delays in the This single paradigm concerns the complexity of com- processing are on the order of hundred milliseconds putations being dependent on the number of variables and must be overcome to match decision making with that gives rise to a search space scaling with that num- the control actions that follow. In addition, the related ber in the exponent. The base of the exponent is known sensory signals may travel an additional hundred mil- to be less relevant, and we set it to 2 for the sake of liseconds or so before they can influence the model. simplicity. In turn, decisions occur long before the deterministic There is an astonishing fact, a peculiar feature of model ‘knows’ about them. Sensory information about human knowledge: It takes approximately 20 years the outcomes has considerable delays, too. Nonethe- for an individual to learn a considerable portion of less, consciously available information, i.e., the internal the available scientific knowledge that humankind has model, compensates for both in a way that makes it collected in the past 20,000 years. Thus, much less seem as if internal decision making and the related time is needed to communicate knowledge than to effects occur simultaneously, with a precision of a few discover that knowledge. Since this is not a necessity milliseconds. Long-distance gamma synchronization in complexity theory, we ask how this might happen. and theta interplay seem responsible for Consider the space of sensory information, the space managing this outcome (Melloni et al., 2007). of memory span, and the space related to languages. Language is instructive for us since it offers a way The sensory space is gigantic. For example, the number to overcome the memory span bottleneck. Constraints of variables perceived on the retina equals the number posed by language give rise to certain conditions con- of , which is on the order of one million. This cerning learning, the execution of control and related defines the search space to be compared with the hu- constraints on communication: man memory span, which is between 5 and 9 items 1. Complex control programs spanning a longer time at one time. Thus, 21,000,000 is the search space, and interval should be learned. ≈ 22 − 23 is the subspace that can be addressed at 2. Means for launching learned control programs one time. This exponentiation of the variables is called should exist. the curse of dimensionality, a term coined by Richard 3. The number of entities to be controlled indepen- Bellman approximately 60 years ago. It is hard to ap- dently at one time cannot be large. preciate such numbers. To give an example, folding 4. Switches between control programs cannot be fre- a piece of paper (only) 50 times would give rise to quent. an object with thickness about as large as the Earth distance from the Sun. These conditions may be satisfied if processes in the One can communicate well with 2,000, i.e., ≈ 211 world can be segmented (separated) in space and time, words. The space that language can cover is very i.e., if there are separable episodes. large since sentences or documents can contain many Consider memory formation and memory manipula- words, and the number of words in a communication tions. We have procedural (implicit) and declarative multiplies the exponent. A relatively short document (explicit) memories. The latter are made of seman- may have 5,000 words; therefore, in principle, it is tic and episodic memory forms; see, e.g., (Buzsáki a member of space where the number of members and Moser, 2013)and the references therein. Declara- is 211∗5,000 = 255,000. This temporal compositionality tive memory includes facts, , and autobiographic increases the expressive power of any language, includ- episodes, among other forms. These forms are first ing sign language and body ‘language’, for example. learned in the relatively small hippocampal-entorhinal The meaning of any expression is ambiguous, and its complex (EHC). Then, they are transferred to neocorti- context, i.e., the situation, the other expressions of the cal areas for long-term storage. Learning, transfer and communication, the experiences of the partners indi- usage should satisfy the constraints posed by the needs vidually or together, and the goal of the communication of the predictive deterministic model. Uncertainties may disambiguate its meaning. Such disambiguation must last only a few milliseconds at each brain region is also a cognitive task. to ensure timely actions despite the large delays in the Another crucial matter is that our world, including distributed computational system of the brain. the inertial motion of our body, to a good approxima- The EHC is critical for learning, encoding, and trans- tion, is deterministic. In turn, a deterministic model ferring memories. The plays a central role (a.k.a. a representation or interpretation) of the world in cognitive manipulations. These and the other brain should apply, including the inverse dynamics that con- regions compute together. Brain oscillations orches- trol the body. However, the deterministic model differs trate and synchronize the computations involved in from a Cartesian theater (Dennett, 1991) since it is processing sensory information, launching actions and a compressed approximation of the world, which is computing the deterministic representation. Nature not fully observed. Ambiguities are present, and the has developed these brain oscillations. There are six

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different frequencies, which span the 0.1 Hz – 100 Hz may become a component in a cognitive task. The con- range, and their ratios have approximately the same cept of component serves to define semantic memories, values for all mammalian species, despite the large dif- episodic memories, and the multi-scale relationships ferences in brain size between mice and elephants, for between them. We elaborate on the concept of compo- example (Buzsáki and Watson, 2012). nents below. The strict requirements for the computations in the brain can be corrupted in diverse ways. For exam- 2.1 Components: an overview of the ple, the frequencies or strengths of brain oscillations may change, the amplitudes of these oscillations may concept go out of control, the wiring may be corrupted, and It is central to describe the concept of components, the selective procedure may be weak, to name a few sometimes called factors, descriptors, or features, to possible types of corruption. Such corruptions have dis- explain why they are relevant and determine the cri- tinctive names, such as schizophrenia, epilepsy, micro- teria of a good descriptor. Another concern is how or macrocephaly, noisy brain, and others. However, components can be applied in different goal-oriented if they are weak, but occur in combination, then the contexts. As an illustration, consider the concept of associated distinctive phenomena can corrupt learning, numbers. manipulation and the control of more complex tasks, The central feature of number theory is generaliza- such as language and social behavior. Social behavior, tion. When we use numbers, we strip off a large variety for example, involves dynamic internal and external in- of descriptors and focus on the additivity property in- puts - at least two players, the target of the interaction, dependent from the situation. Fig. 1 illustrates that the interests, goals, strategic thinking about reaching the same equation may occur when planning a vacation or goal, an understanding of the other player’s feelings when considering a chemical reaction. and beliefs, problem detection, and rapid decision mak- ing to seek solutions or compromises when partners (a) face barriers. Additionally, internal factors, such as mood and motivation, may be influenced by other vari- ables. The co-occurring impairments can corrupt any element on that list, and comorbidities can manifest 2 weeks (climbing) + 1 week (sailing) = 3 weeks (vacation) in diverse ways. In our view, this is autistic spectrum 2+1 = 3 disorder (ASD). We shall proceed as follows. In the next section, we (b) consider the problem of component forming. This is CaCl2 + CO2 + H2O  CaCO3 + 2 HCl followed by our ‘results’, in which we specify disorders 2 oxygen atoms + 1 oxygen atom = 3 oxygen atoms that can contribute to autism. In the discussion, we 2+1 = 3 consider ASD models, what these models are missing, Figure 1: Numbers are special concepts. and why and how our model covers and complements Numbers are components and can be combined with them. We draw our conclusions and provide an outlook other descriptors (components) in diverse ways. (a): in the last section. Both climbing and sailing may be part of a vacation. (b): Oxygen is a constituent of carbon dioxide, water, and calcium carbonate 2 Theoretical background in the Numbers are special abstractions since they can be context of experimental find- used diverse contexts, e.g., societal, biological, chemi- ings cal and physical ones, but the other specific concepts of those disciplines are not needed for the derivations. Let us start with basic thoughts on cognition and clar- Nonetheless, a number always corresponds to a fea- ify our abstractions. We use the word ‘cognition’ to ture and may lead to different conclusions in different describe processes that manipulate and control a set of disciplines. For example, the concept of distance is an mental models equipped with features that represent additive quantity, and non-additivity leads to a special past, ongoing, or planned interactions between them. relativity. Additivity also applies to the Mendelejeev Thus, features are related to the interaction, and fea- table, as in the chemical example in Fig. 1. Chlorine, tures unrelated to the interaction or its goal can be for example, can play a role in many compounds, and neglected. A low-level example is eating; the choice the balance of sums constrains the reaction. A compo- of the object to be eaten depends on whether it has nent can play roles in different episodes. The related the feature ‘edible’. Edibility is only one feature out metric that can be represented by numbers and thus of many, but it may be sufficient for decision-making different kinds of equations constrain the episodes. In and action at a given time. The feature ‘edible’ be- our formulation, a component is an abstract feature, longs to many objects, and many cognitive tasks may and it has a metric. The metric can be discrete, as need to consider this feature. In other words, a feature in the case of chlorine, or continuous. We shall use

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this definition in the context of place cells, also called a resolution to the fallacy and an architecture, the place fields, for example (O’Keefe and Nadel, 1978) autoencoder made of the input, the representation, that make an approximate discretization of the spa- and the generated (estimated) input. We consider au- tial component as well as the related grid cell system toencoders in the context of making sense or central (Rowland et al., 2016) that compute the related metric. coherence. The transformation of one representation Together, these components can comprise a predictive to another is called mapping. deterministic model for cognition, e.g., if the compo- Mapping connects or associates two representations, nent is represented in the neuronal substrate and, e.g., like connection between the curved surface representa- if it is equipped with attractor dynamics. tion of the Earth and a two-dimensional flat represen- We model the representation that discretizes a com- tation. The autoencoder is made of (at least) two types ponent and the dynamic system that computes the met- of mappings. The first maps the input of the repre- ric with a predictive autoencoder. In this setting, place sentation. The other one maps the representation and fields comprise the hidden representation, and grid produces an estimation of the input. Both mappings cells contribute to the computation of the estimated are to be learned. The representation makes sense at future inputs given past ones. The concept of autoen- a given time if the input it generates closely resem- coders will help us to shed light onto the enigmatic bles the actual input at the time of the comparison. A problem of making sense. relevant condition in this is that delays in processing should be counteracted; we require that the input that 2.2 Making sense with autoencoders produces the representation and the estimated input generated from the representation should match each We will use concepts developed in artificial neural net- other at any given time, despite the time required by works as our tools for understanding autism. The first both mappings. This constraint leads to the need for step is to examine how concepts related to autism may predictive mapping. emerge in these structures. We start from the Weak In sum, making sense requires an architecture that Central Coherence (WCC) theory(Frith and Happé, (i) maps the input to the representation and (ii) prop- 1994; Happé and Frith, 2006). agates the representation in time into the future (iii) According to the WCC theory of autism, people with in order to match the representation-based estimation ASD are impaired in making sense. We may suspect of the input to the actual input in spite of the delays. what this claim means but without an algorithmic Thus, sense-making can be corrupted in at least four and thus computerizable definition, the claim is not ways: amenable to modeling and is blurred, more so since (1) if the input-to-representation map, a.k.a. encod- the algorithmic process that models making sense has ing or bottom-up processing, is erroneous been philosophically enigmatic. Searle has asked two (2) if propagation into the future, a.k.a. the predic- related questions: tive system or recurrent collateral-based temporal Q1 . . . If we are to suppose that the brain is a digital encoding or model, is erroneous computer, we are still faced with the question ‘And (3) if the representation-to-input map, a.k.a., decod- who is the user?’ (Searle, 2013) ing or top-down modulation, is erroneous Q2 The brain is working with internal representations, (4) if action generation based on the representation but internal representation in any information that makes sense is erroneous because the final processing system is meaningless without an in- error between the input-to-estimated input is not terpreter. The homunculus paradox claims that an error of a single autoencoder embedded deeply all levels of abstraction require at least one fur- into parallel-distributed processing but is an er- ther level containing the corresponding interpreter. ror between the planned action and the action The interpretation – according to the fallacy – is that occurs and is observed. This loop of obser- just a new transformation, and we are trapped in vation, computations and action launching has an endless recursion (Searle, 1992). considerable delays that must be counteracted by predictive autoencoders. Fortunately, the fallacy can be solved by rephrasing the sentence in interpretation and turning the fallacy There is a tacit assumption here, namely, that sense- upside down (Lőrincz, Szatmáry, and Szirtes, 2002; making involves conflict resolution, since conflicts may Lőrincz et al., 2002). Searle said that the representa- arise between parts or components of the representa- tion of the input should make sense. The rephrased tion. Thus, we assume the presence of components version is this: (not the internal representation but) and the previous and ongoing learning of them. We the input, e.g., the retinal pattern, or its transformed assume that representations and estimations of the di- forms, should make sense. We say that the input makes verse components are imprecise, but decision-making sense if it can be derived or generated internally, i.e., requires the resolution of conflicts between them. In by means of the internal representation. According to turn, we have a fifth and sixth possibility for potential this approach, the internal representation interprets impairments: the input by (re-)constructing it. In turn, we have (5) if component formation is imprecise or corrupted

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(6) if timely conflict resolution is suboptimal or im- The efferents of these layers target different substruc- paired. tures of the , such as the (DG), the CA3 and the CA1 subfield. The output of All the underlying neural mechanisms may be cor- the hippocampus is the input of the deep layers of the rupted and may contribute to ASD. For example, an EC. Efferents of the EC’s deep layers close the loop error may occur if component formation concerns the by targeting the superficial layers of the EC. Efferents binding of multi-modal sensory inputs or if model con- also spread throughout the , possibly to close struction and prediction, and thus temporal propaga- larger loops in a direct or indirect manner. For a de- tion, in the internal, possibly attractor network is im- piction of the anatomy of the hippocampus and its precise. pathologic conditions, see (Dekeyzer et al., 2017). For more details on the evolution of this structure and on 2.3 The neural system of component its environment, see (Witter, Kleven, and Flatmoen, learning 2017) and (Witter et al., 2014), respectively. Grid cells appear in the medial Any component corresponds to an abstraction as op- and form a triangular attractor network that precisely posed to sensory information. Such abstractions can measures any changes in position. For a review of simplify cognitive tasks provided that the relevant com- grid cells, see (Rowland et al., 2016). There are two ponents are selected for solving the problem. A com- types of these grids. The responses of one of them are ponent is of high value if helps to decrease the number head direction independent. Other neurons exhibiting of required components. An example is a coordinate triangular activities are sensitive to head directions. system that is tightly aligned to the environment, a.k.a., The interdependence of grid cells and place cells is laboratory coordinate system or allothetic system, the complex; they assume each others’ roles, and spatial counterpart of the egocentric or idiothetic system. representation becomes corrupted (in diverse ways) if any of the constituents of the loop is lesioned, even if 2.3.1 The discretization of the allothetic space the deactivation is only transient. component Head direction cells (Taube, Muller, and Ranck, 1990) are present in different brain structures, includ- Place fields are neurons that respond to overlapping ing the entorhinal cortex (Taube, 2007). They are but small local regions in the available space, and their necessary for the formation of both place cells and grid responses in that region are almost independent from cells (Taube, 2007). This specific feature was exploited light conditions, head directions, body position, and as a semi-supervisory signal in the deep learning model gaze direction, among other factors. In turn, they dis- that developed both place cells and grid cells from cretize the available space and strip off peculiarities visual and head direction information (Lőrincz and of the individual fields. The discretized space is typi- Sárkány, 2017). cally two for rats (O’Keefe and Burgess, 1996) and can The responses of place fields are useful high-level be three for bats (Yartsev and Ulanovsky, 2013). In abstractions, justifying the use of the name ‘cognitive turn, place fields and the corresponding metric form a map’ for the hippocampal CA1 and CA3 subfields where component. these neurons reside. Discretization of the allothetic co- Place field representations occur in the hippocampus, ordinate system alleviates the curse of dimensionality one of the main parts of the entorhinal-hippocampal and makes path planning (exponentially) easy. complex (EHC). The entorhinal cortex contains grid cells and head-direction cells that, together with the 2.3.3 Episodic memory from temporal relations place cells, can give rise to an implicit representation of the metric (Moser, Moser, and McNaughton, 2017). It is generally believed that declarative memories de- Many details of the EHC structure are known, and pend on the EHC loop (Buzsáki and Moser, 2013). the structure encompasses almost all problems that, Episodic memory encodes temporal sequences and can in diverse combinations, may lead to ASD and may replay them. Replays are launched in the CA3 subfield also occur in and between neocortical areas. Below, of the hippocampus via its large recurrent collateral we describe the EHC, which forms a loop. It has been network system. Replays have a very specific waveform interpreted as an autoencoder (Lőrincz and Buzsáki, and create the most synchronous known population 2000; Chrobak, Lőrincz, and Buzsáki, 2000). pattern in the brain (Buzsáki, 2015). They occur dur- ing consummatory behavior and non-REM . Their 2.3.2 The loop particular waveform is called sharp waves (SPWs) and ripples or SPW ripple complexes. SPWs have large am- The hippocampus is part of the loop. It is a relatively plitudes and a negative polarity. They are 40-100 ms small archicortical structure at the top of the sensory long. In many cases, they are accompanied by ripple processing pathways. Some of its inputs arrive from waves of 110-200 Hz. different sensory modalities through the neocortex to SPW ripple complexes are necessary for memory con- the superficial layers of the entorhinal cortex (EC). solidation and the transfer of memory to neocortical

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areas. They have high excitatory gains for efficient resentations (Lőrincz and Sárkány, 2017). We quickly transfer, and these high gains may give rise to epilep- review why we adopt this view. togenic episodes; indeed, the hippocampus is the most In rats, spike sequences during theta cycles are time- prominent source of epileptic high-frequency oscilla- compressed versions of the place fields belonging to tions (Bragin et al., 1999). the rat’s trajectory (Dragoi and Buzsáki, 2006; Lisman and Jensen, 2013). This compression in the entorhinal- 2.3.4 Forming semantic memory by factoring hippocampal loop is the result of theta phase modu- out time lation of gamma wave power; see, e.g., (Buzsáki and Moser, 2013) and the references therein. Theta oscil- In addition to temporal sequences or episodes, seman- lations are in the 6-10 Hz interval, whereas the fre- tic memory is the other type of explicit memory formed quency of gamma oscillations is between 30-120 Hz. by the complex around the hippocampus. Our view on The time constant of the neurons receiving these mod- semantic memory is very similar to the view described ulated signals is between 10-30 ms, and thus, synapses in (Buzsáki and Moser, 2013). The putative ‘algorithm’ formed with those neurons can send spikes during is widely used in natural language processing and im- this relatively long interval. Since cell assemblies of age processing, where it is called ‘bag of words’ and the hippocampus have a lifetime of approximately the ‘bag of visual features’ representation, respectively. We same duration (10-30 ms), neuronal assemblies that describe the former. Documents contain many words fire within this time window will be learned by the organized by time according to syntactic rules. The cat- downstream neurons receiving their signals without egory of the document can be classified quite accurately any information about their temporal order. As it hap- by means of the number of occurrences of the words pens, neurons responding to a trajectory of approxi- in the document upon normalization. Many words mately 1 s travel time fire within that 10-30 ms time will have zero occurrences, but contextually important window. Context is also available for associative learn- words will have high values. Time and thus syntax ing; since the positive cycle of the is approx- are neglected in this representation, and only the co- imately 100 ms, it has a number of gamma oscillations, occurrences are retained. This concept is similar to and the time they can cover in 100 ms is on the order the notion of ‘recognition-by-components’ (Biederman, of approximately 2 s. 1987), in which a structure, e.g., a face, is recognized as a face by the components it has, sometimes even in the presence of syntactic errors. In turn, constituents 2.4 Consciousness and component form one part of semantic memory. In addition, the binding meaning of a word can be disambiguated by the list of other words surrounding it, see (Pintér et al., 2015) Components may be observed through different modali- and the references therein. The difference between the ties. Such signals propagate through different channels set of constituents without and with the context, in the and can have different delays. Delays in a synchronized case of partial observation, is illustrated in Fig. 2. Thus, autoencoder are compensated by predictive structures, we define the semantic memory belonging to an entity and consequently, delays in the different processing as the time-independent collection of its constituents channels can be matched. This is a sophisticated multi- and the context of which that the entity is a constituent. purpose algorithmic structure in the neuronal archi- Semantic memory forms an associative structure that tecture, and each of the elements may be corrupted, is robust against timing requirements. disrupting cognition. There are multiple options for interpretation, but only one may survive at a time, and selection seems occur unconsciously. This problem of unconscious selec- tion and disambiguation was noted by von Helmholtz approximately 150 years ago. He based his arguments on multistable visual , demonstrating that under certain conditions, there should be only a single interpretation at a time. Figure 2: Components and context dependence Due to the different delays in the different process- Spatio-temporal context can modify the interpre- ing channels, the single – typically time-dependent – tation. (a) Interpretation of the structure defined interpretation cannot change quickly but should be by its components: A soccer ball that needs inflat- maintained for at least the duration of the cumulative ing. (b): Interpretation of the same structure in its delays in input-output processing, i.e., for delays be- context: A chair. tween the inputs of sensory channels and the motor outputs. Such constraints are plausible since the world We reinforce the view that time compression is a is deterministic. Experimental findings support our key to understanding the time-independent nature of view: Switching in multistable perception binocular semantic memory that leads to efficient bag-like rep- rivalry experiments can take as long as 1 s (Leopold

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and Logothetis, 1996; Brascamp et al., 2005). For a 2.5.1 Brain waves contribute to memory consol- recent review, see (Brascamp et al., 2018). idation in many ways

In the case of binocular rivalry, the single interpreta- Due to spike time-dependent plasticity, proper timing tion of the interpreting autoencoder is ‘in error’, since of neuronal spikes is critical for long-term potentiation the other potential interpretation is diminished until and the long-term depression of synaptic strengths. perception switches. Such mistakes cannot be avoided There are stringent requirements of memory learning if a single interpretation is required. Imprecise delay and consolidation, since (i) they require two stages, compensation, however, can lead to different errors the exploratory and the consummatory stages, and (ii) that may give rise to . We shall consider there are two types of memory formation tasks, namely, such issues later. the learning of temporal series and the learning of According to theoretical predictions (Lőrincz and semantic relations. Buzsáki, 2000; Lőrincz and Szirtes, 2009), delay com- Theta waves are present in the exploratory stage. pensation depends on the hippocampus, which also Their presence poses considerable challenges since time drives the learning in neocortical structures. Lőrincz is needed for both learning temporal series and tempo- and Buzsáki suggested that delay lines in the loops ral compression for learning semantic relations. The formed by the dentate gyrus and CA3 serve the adap- process is controlled by the two efferents of the EC tation process needed to compensate for the signals entering the hippocampus: Neurons of layer 3 of the within the hippocampus and those that arrive from EC innervate the CA1 subfield, whereas efferents of the neocortex, including the entorhinal cortex. The layer 2 entorhinal neurons enter the CA3 subfield. The assumption is partially supported by the experiments interplay between the two channels controls spike tim- of Henze et al. (2002). They found tunable and long ing and the coupling between theta and gamma waves delays in this loop. More recent results provide further (Fernández-Ruiz et al., 2017). support: Khodagholy et al. (2017) monitored multiple The consummatory stage is also complex since mem- sites in the rat neocortex and found that (i) ripples in ory consolidation should involve both the EHC loop the association cortex were similar to hippocampal rip- and remote sites in the neocortex. Sharp waves (SPWs) ples, (ii) these ripples were coupled and (iii) coupling play an important role in these processes. However, was strengthened during sleep, suggesting that delays SPWs are not the only player; other brain waves con- were compensated for dynamically. tribute to this process, but the full machinery is not yet We consider the binding of components a ‘synonym’ known. Some details follow below. for the learning of spatial and possibly temporal con- According to the experiments, slow-wave sleep texts, or both, that can be multi-modal and may be (SWS) seems to be more involved than REM sleep in subject to delays. The context learning depends on the consolidation of the system, but both types of sleep REM sleep, as indicated by the REM-specific optoge- contribute (Diekelmann and Born, 2010; Born and Wil- netic silencing of GABAergic neurons of the medial helm, 2012). SWS or non-REM sleep is characterized septum projecting to the hippocampus in mice, which by joint spindle and ripple events. During these events, diminished learning (Boyce et al., 2016). the depolarizing up phases of slow oscillations reac- tivate the hippocampal time series and SPW ripples. Different brain wave frequencies can couple, and slow waves seem to restrict the time interval for coupling when faster oscillation occurs within slower ones, a 2.5 Two-stage operation: Memory en- phenomenon called phase biasing. In turn, slow oscil- coding and consolidation lations can orchestrate local processes globally, making SPW ripples the most synchronous wave pattern in the Further complications arise from the need to encode brain. Another observation is that these ripples can sta- memories, transfer them to neocortical areas and con- bilize the place cells in the hippocampus (Roux et al., solidate them. There are two stages of explicit memory: 2017). Experiencing and consolidation. They occur separately, However, Boyce et al. (2016) showed causal evi- as first suggested by Buzsáki (1989). He suggested dence that contextual memory consolidation depends that during exploration, i.e., during the theta waves, on theta rhythms during REM sleep: they developed neocortical information enters the hippocampus and in- a REM sleep-specific optogenetic silencing method for duces weak and transient heterosynaptic potentiation medium septal GABAergic neurons and showed that in the CA3 subfield. Later, these weakly potentiated silencing during REM sleep impairs the formation of neurons and neuronal chains cause sharp waves during fear-conditioned contextual memory. In this regard consummatory behaviors that give rise to long-term it may be worth mentioning that according to simu- synaptic modifications in the hippocampus. This model lations (Lőrincz and Sárkány, 2017) both temporal has gained considerable support over time on both theo- and semantic memories can be important for compo- retical (Hinton et al., 1995) and experimental grounds. nent formation accompanied by the development of Recent experimental support is reviewed below. the related metric, important elements of cognition.

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2.5.2 Consolidation of emotional components C3) Component formation is to be accompanied by the development of a metric for the components to The consolidation of emotions in their respective con- enable model constructions, predictions and ‘path’ texts involves additional brain structures beyond the planning. EHC and the neocortex. There is general agreement C4) Component formation requires the synchroniza- that emotions are processed by the , and tion of inputs with the representation and the anatomy supports this additional structural require- representation-based estimation of the inputs. ment: This triad forms a loop made of (i) bottom-up (i) the ventral hippocampus projects directly to the (downstream) processing, (ii) predictive (recur- basolateral amygdala and the central amygdala, rent collateral based) processing, and (iii) top- and connections are reciprocal (Pitkänen et al., down (upstream) estimation. The loop enables 2000), model construction in the representation, tuning (ii) fear can be and off in distinct circuits and synchronization given the error between in- between the amygdala, hippocampus and the me- put and estimated inputs; see, e.g., (Lőrincz and dial prefrontal cortex by selective activation of Szirtes, 2009) and the cited references therein. specific neuronal circuits (Herry et al., 2008), and Now, we turn to constraints on the network level. (iii) multiple parallel pathways exist between the These constraints emerge from considerations regard- amygdala and the hippocampus (Xu et al., 2016). ing neural firing and synaptic energy consumptions, One pathway encodes the context-dependent re- wiring costs and, last but not least, the characteristics trieval of cued fear memories. Another path- of natural signals. way is concerned with fear behavior in a context- dependent manner. 2.7 Sparse representations In turn, fear is an internal component that character- izes both episodic and semantic memories. Neural computation is ‘expensive’; the brain consumes Experimental studies on the consolidation of emo- considerable energy. There are two main contributors tional components have been published recently (Gi- to energy consumption: Neuronal spikes and synapses. rardeau, Inema, and Buzsáki, 2017). Their findings Fifty percent of the energy is used on postsynaptic glu- that reactivations of memory traces in the basolateral tamate receptors, 21% on action potentials, and 20% amygdala peaked during hippocampal SPW ripples are on resting potentials; energy is also spent on presy- in line with other consolidation patterns. naptic transmitter release and transmitter recycling (Howarth, Gleeson, and Attwell, 2012; Harris, Jolivet, and Attwell, 2012). Energy insufficiency may involve 2.6 Bottom-up and top-down process- several neuropathologies. ing serves component formation The conditions for the learning of components are com- 2.7.1 Theoretical considerations regarding spar- plex and are listed below: sity

C1) The representation of any component is to be Given the considerable costs of neuronal resting poten- made free from other components. For exam- tial, and postsynaptic receptors, the ple, shape may not involve color, and form should neural system should minimize firing. Connections re- be separated from motion. However, behavior is quire volume, and propagation along the axons and based on the different behaviorally relevant com- takes time, giving rise to limitations in the ponents, and the interpretation of these compo- size of the brain. In turn, connectivity should also nents depends on the context; thus, they require be sparse. How can such constraints support cogni- associations between the separated components. tion? Sparse representations seem to be related to the Such associations between components look simi- characteristics of the statistics of natural signals. Such lar to multi-modal information fusion, with infor- statistics have a general feature; they exhibit power law mation fusion providing the separation and the behavior (see, e.g., the work of Ruderman (1994) on pattern completion capabilities between the com- images and that of Lewicki (2002) on sounds as well ponents and within the components, respectively. as the references they cite). Neuronal nonlinearities Figure 2 serves illustrative purposes in this respect. are well adapted for such signals (Schwartz and Simon- C2) Components – in the ideal case – come in two celli, 2001), and the proper compression method for different forms. Episodic components address au- such signals with heavy-tailed distributions is sparse tobiographic memories and are restricted in space representation (Olshausen and Field, 1996), especially and time. Semantic components differ; they are since it meets the constraints on energy consumption. non-episodic and represent a combination of com- Furthermore, searches for optimal components of the ponents, are influenced by the context and are sparse representation are generally subject to combi- independent of time. natorial explosion, but it is not a necessity for com-

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ponents assuming heavy-tailed distributions due to autism. We shall return to this point later. a good approximation called L1-Magic (Candès and GABA concentrations correlate with the efficiency of Romberg, 2005). Furthermore, learning in deep sparse the suppression of neural activities, although in a non- artificial neural networks has favorable mathematical uniform way, as they are dependent on the ongoing convergence properties (Arora et al., 2014), suggest- cognitive tasks. For example, in the case of voluntarily ing that evolution found a tractable solution for com- suppressing memory retrieval, (i) reduced hippocam- ponent learning and exploitation based on the data pal activity and reduced memory for suppressed con- structure that fits cognition with limited memory span tent was found, (ii) greater resting concentrations of constraints. In addition, the neuronal structure and dy- hippocampal GABA predicted better mnemonic con- namics are well adapted to power law distributions and trol, and (iii) higher hippocampal GABA predicted to related scale-free small-world structure and, on the stronger fronto-hippocampal coupling during suppres- modeling side, to representing the dynamic processes sion, whereas (iv) the level of prefrontal GABA did of such structures; see, e.g., (Singer, 2013; Cocchi et not influence the effect (Schmitz et al., 2017). We con- al., 2017) and the references therein. clude that voluntary control and thus the manipulation of unwanted components require greater GABAergic 2.7.2 Constraints and errors in sparse represen- influence. tations As previously mentioned, coupling and coherence between brain waves seem critical for memory forma- In a typical network with sparse representation, the tion and consolidation. Recently, Ognjanovski et al. input excites all neurons downstream first. Then, the ((Ognjanovski et al., 2017)) studied single-trial con- generative process generates an estimated input, as textual fear conditioning. They found that network in the model of Olshausen and Field (1996). The dif- oscillations in the CA1 subfield showed greater firing ference between the input and the estimated input is coherence with fast-spiking interneurons. Fast- spiking used to correct and sparsify the representation, and interneurons are GABAergic interneurons that most the interaction continues in a second generative step likely contain . The inhibition of parval- and then further on. Thus, in addition to consuming bumin -expressing interneurons blocked the consolida- high amounts of energy consumption, forming the rep- tion of fear memory. Blocking also involved losses in resentation is time consuming. Sparse representation delta, theta and ripple waves and CA1 network coher- can take advantage of the generative step, and the en- ence. However, when parvalbumin interneurons were coded memories are similar to those found in the brain activated rhythmically, network coherence improved. (Olshausen and Field, 1996; Lewicki, 2002). The interplay between neuromodulatory con- There are different solutions to the abovementioned stituents and neurotransmitters is also complex and problems. For example, one may use sparse represen- delicate. Different molecules may act and balance each tation for the supervisory training of a deep neural other in very specific ways. For example, deletion of network. The result will be much faster (Sprechmann, neuroglinin-3, a postsynaptic cell adhesion molecule Bronstein, and Sapiro, 2015), and the solution has the (Polepalli et al., 2017), decreases NMDA receptor- promise of large gains in energy consumption. The mediated postsynaptic currents and increases the prob- drawback is that many layers may be needed. ability of presynaptic glutamate release in parvalbumin It seems, however, that neuroscience has found other interneurons. The net outcome of the changes is that solutions and uses fast feedforward and feedback com- gamma oscillations and sharp wave ripples are reduced, petitive inhibitions; nature ‘developed’ a large set of and the extinction of contextual fear memories is de- different interneurons. Neural representation in the creased, whereas fear conditioning does not change. hippocampus is highly sparsified, and inhibitory cir- Since the energy demand of fast network oscillations cuits form a sophisticated network that incorporates a is high (Kann, 2012) and since parvalbumin-containing considerable number of different interneurons (Freund basket cells make approximately 50% of the interneu- and Buzsáki, 1996). One may suspect that it is hard to rons capable of generating such oscillations, one ex- balance the excitation/inhibition ratio in the different pects that an energy deficit of parvalbumin neurons stages of exploratory and consummatory behaviors and leads to diverse impairments in functioning, a finding the consolidation processes of episodic and semantic supported by recent experiments (Inan et al., 2016). memories. Another solution for saving energy and gaining speed 2.7.3 Autoencoding and interpretability is to group similar neurons and set up competition among them. This is the so-called structured sparse As previously noted, sparse representation is formed by representation (Bach, 2010). This structure offers fast the autoencoding principle. This is so since sparsifica- feedforward estimation of the groups that are to be tion of the representation alone is fragile and may give selected (Lőrincz et al., 2016). Lőrincz et al. (2016) rise to low quality or possibly zero activities, whereas considered structured sparse representation as a model the autoencoding principle forces the network to avoid of minicolumnar organization. This minicolumnar or- such trivial representations. The quality of the repre- ganization is another potential source of problems in sentation can be maintained if the representation is

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supposed to save all or most of the information about connections; see, e.g., (Courchesne and Pierce, 2005), the input, i.e., if it can generate a high-quality estima- for a review. tion of the input. We shall call the difference between The minicolumnar organization of superficial layers the input and the predictive estimation of the input 2 and 3 of the neocortex is not yet understood, and it reconstruction error. Sparse representation compresses has been debated whether it has a function at all or the input if the number of active (spiking) neurons in it is only a consequence of the distribution of double- the representation is smaller than the number of active bouquet cells in those superficial layers (Horton and units in the input. This is called sparse compression. Adams, 2005). There is significant variation in distri- Similarly, L1-Magic is also called compressed sensing to butions in different areas, including the portion of the emphasize this particular property; see, e.g., (Donoho, ratios of the two subtypes of these double-bouquet neu- 2006) and the cited references. rons (Yáñez et al., 2005). The same paper also reports Sparse compression differs from general data com- that these GABAergic interneurons are not present in pression methods, such as the Zempel-Liv algorithm , lagomorphs, and artiodactyls and are present in PKZip, Gzip and png, since it must regenerate the but in relatively low numbers in carnivores. There is a input using only a few active neurons. Component lack of consensus regarding the size, structure and in- learning, then, is a natural tendency of this method; it teraction of the minicolumnar structure and the related is often called sparse dictionary learning (Rubinstein, changes during development. Zibulevsky, and Elad, 2010) because of the analogy According to some works, the minicolumnar struc- that although a dictionary contains many components, ture is reduced in autism; the total number of neurons a few components can make a sentence. The better the in individual minicolumns seems normal, but the dis- autoencoding property and the sparser the representa- tance between minicolumns appears to be reduced tion, the lower the energy consumption and the better (Casanova et al., 2006). Other works present differ- the dictionary, provided that information is spared. ent results. For example, McKavanagh et al. (2015) report that wider minicolumns are more pronounced 2.7.4 Impairments in the neuronal networks in younger ages in ASD. The origin of the discrepancy is unclear. Page et al. (2018) report that a gene associ- Since we assume that (i) impairments in social behavior ated with both schizophrenia and autism regulates the are the result of corrupted component formation and columnar distribution in the prefrontal cortex, and this exploitation and (ii) component formation modifies regulation is activity dependent. Experimental results brain connectivity, there are at least two associated indicate that environmental effects and potential differ- falsifying issues: ences in the cohorts of autistic individuals may account for the discrepancies. Developmental trajectories may 1. neuronal development is atypical in autism and indeed differ, as reported by Tek et al. (2014) and as impairs network modifications and thus compo- indicated by studies of discordant monozygotic twins nent formation, (Willfors et al., 2017). A detailed discussion of the 2. neuronal activities are atypical, and the activity different findings regarding organization can be found patterns impair the exploitation of components. in (Dickinson, Jones, and Milne, 2016). They consider Such differences can lead to different types of autism the diversity of abnormalities associated with ASD and and different comorbidities. We review brain structure the number of individuals included in the experiments. impairments below. 2.7.6 Differences in axial diffusivity, myelina- 2.7.5 Brain structure impairments tion, and synaptic homeostasis Atypical neuronal connectivity structure means atyp- Ecker et al. (2016) also found that increased gyrifica- ical development. This atypical development might tion is accompanied by atypical neural axial sprouting, occur at different times, e.g., during early develop- which is most pronounced in axons traveling close to ment or later, and both are general features of autism. the cortical sheet. According to their report, enhanced Larger brain sizes have been noted in individuals with gyrification correlates with increased axial diffusivity ASD, and Hazlett et al. (2017) suggests that the atyp- in general, and the relationship may be causal in either ical increase in brain volume occurs early during de- direction. Such changes may influence the non-linear velopment. They found that surface area information dynamics of the brain, which execute highly intercon- form magnetic resonance images of the of 6- nected parallel and serial operations close to the so- to-12-month-old children has a high power to predict called critical region characterized by spatio-temporal whether the child will later be diagnosed with autism. avalanche dynamics; these dynamics are heavy tailed, Larger surface areas of the same volume involve have close to a 1/f power distribution, the power of different gyrification patterns, as have been reported brain oscillations is inversely proportional to the fre- in the literature for the left pre- and post-central gyrus quency of the oscillations, as is typical in scale-free (Ecker et al., 2016). Increased gyrification seem to small world structures (Buzsáki and Draguhn, 2004; enable an increase in the number of short-distance Singer, 2013). Changes in intermediate structures

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can form bottlenecks in information processing and formation and the learning of precise models. information sharing, including the formation of new Time compression seems necessary for semantic rep- representations. resentation, and it can factor time out due to the tem- Structural differences can influence information poral features of Ca2+ channels, which stay open for propagation and synchronization in the neuronal sub- a few tenths of milliseconds. Factoring out becomes strate. An additional parameter is myelination. Accord- possible by averaging related temporally compressed ing to Buzsáki et al. (2013), axon size and myelination episodes during the few tenths of milliseconds, the time seem to be the most important factors for the scaling of that adapting synapses are open. Impairment in the du- network oscillations because they determine the con- rations can corrupt component formation in two ways. duction velocity of neurons. One may suspect changes If the temporal window is too short, then averaging can in the scale-free network property due to changes in ax- be corrupted. If it is too long, then overgeneralization ial diffusivity within local regions, especially if myelina- may occur. tion also changes, as described in (Ecker et al., 2016). Episodic memory can be replayed in time, which Furthermore, such local changes may influence the makes it similar to a predictive model; however, it sensitivity and variations of local neural responses, an needs no input. Proper temporal ordering and replay- intriguing feature in autism (David et al., 2016b). ing capabilities are needed for this memory type. Mem- Synaptogenesis is an additional critical matter. ories are organized in a topographic manner in the Changes in spine morphology can be vital in form- neocortex, similar components are close to one an- ing large networks, and troubled synaptic connections other and mutually inhibition is typical. In the absence are considered the main underlying reason for autism of such local arrangement, accidental simultaneous by many researchers (Toro et al., 2010; Penzes et al., firing may lead to false associations via Hebbian learn- 2011). According to the data, increased spine density ing. Too many spikes, e.g., the noisy brain, may lead gives rise to decreases in cognitive functioning in ASD, to similar unwanted associative connections. The lack supporting the view that ASD can be characterized of localization or excessive axial diffusivity may also by denser connectivities locally and hypoconnectivity impair component learning. globally (Hughes, 2007). There are general processes that may corrupt compo- nent learning, such as synapse formation, strong recur- rent brain waves (i.e., that can destroy learned 2.8 Summary of some phenomena that information), temporal discrepancies due to insuffi- may impair component learning and cient myelination, improper synchronization of brain cognitive manipulations waves (e.g., theta and gamma waves during explo- ration) and improper synchronization of slower waves We have collected some factors that may influence during synaptic strength consolidation (e.g., during component learning and manipulation, but the list sleep). is not exhaustive. The starting point is the autoen- The learning of components serves component ma- coder model (Lőrincz and Buzsáki, 2000; Lőrincz and nipulation, such as cognitive control and motion con- Sárkány, 2017). The autoencoder is a comparator that trol. Control actions should be deterministic and re- computes the difference between input and the esti- quire temporal coordination based on perception. In mated input, the latter of which is derived from the turn, perception should be unambiguous, and a single, representation. Comparison requires compensation dynamic interpretation is required always despite the for the processing delays in the loop. Delay compen- delays in both different sensory processing channels sation can be accomplished by prediction. Imprecise and action execution. The interpretation should ex- prediction, imprecise timing, and imprecise spike time- plain (estimate) the input in all modalities and with dependent plasticity can impair component formation. high fidelity time estimations. In cases with more Components have low dimensions that enable dis- than one potential interpretation, these interpreta- cretization of the space of the component, as in the tions should satisfy the constraints of the deterministic case of place cells that form the ‘cognitive map’. Dis- world; while only one interpretation may be present at cretization is made possible by means of highly sparse, a time, interpretations with similar probabilities should possibly almost winner-takes-all representations. A be able to switch. lack of sparsity corrupts component formation. The list of the constraints posed by component for- To control the components – e.g., to navigate in mation and exploitation is as follows: allothetic coordinates – a representation of the met- ric is necessary to estimate the magnitude and the (i) intact bottom-up processing with sparsification, direction of control outputs. Particular forms of met- which requires feedforward and feedback inhibi- ric learning exploit the interplay between semantic tions, as illustrated in Fig. 3, and episodic memories; see, e.g., (Dragoi and Buzsáki, (ii) intact top-down processing for training long-term 2006; Buzsáki and Moser, 2013; Lőrincz and Sárkány, memories, which requires predictive model-based 2017) and the references cited therein. Imprecise inter- synchronization (Fig. 3), play including imprecise timing can corrupt component (iii) predictive models for forward propagation in time,

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which require recurrent collaterals (Fig. 3), varied. ASD has an exclusively behavioral definition, (iv) learning of delays and compensation of delays for and research has uncovered tremendous and divergent multi-modal fusion, potential causes. The behavioral characteristics include (v) intact gamma and theta waves for time compres- the early onset of difficulties interacting with others, sion, both for semantic memory and for encoding which in many cases are accompanied by restricted temporal sequences, interests and repetitive activities. The large set of po- (vi) intact slow waves, spindle waves and sharp waves tential causes is accompanied by a high probability of for consolidation and for the transfer of learned comorbid traits with different behavioral characteris- memories to the neocortex, tics. How can it be that a problem with social behavior (vii) unconscious selection of a single interpretation for may co-occur with impulsive ADHD, epileptic seizures, conscious observation, hallucinations, and synesthesia as well as problems (viii) unconscious synchronization of conscious obser- with goal-directed actions? vation of the perception-action loop for precise We have previously proposed that the common rea- action planning, launching, and plan execution, son is the formation and exploitation of components. (ix) unconscious switching between alternative inter- Components are complex and uncertain, especially pretations, but not sooner than the integrated sum when they are hidden. Nonetheless, the selection of of the delays and synchronization time in both the smallest number of relevant components is critical the sensory processing and action execution path- in decision making. This is the case in social inter- ways, actions, in which estimations of mood, feelings, level (x) discretization of the learned separated compo- of knowledge and capabilities are necessary for the nents in the same manner as place cells, success of joint activities. In the previous section, we (xi) separate and synchronized prediction of the listed the neural processes needed for the learning and learned components, as illustrated in Fig. 3(b), manipulation of components. In this section, we con- and sider the underlying reasons for the comorbidity of (xii) modulation of inputs at will, i.e., the focus of the different disorders. We consider justifications or rea- attention process needed to decrease the combi- sonings regarding comorbidities one of the strengths natorial variations to disambiguate the represen- of our model, especially since this is the weak point of tation within the context of the actual task. most models. We shall consider other models in the Discussion. Component learning and component exploitation We start with an observation that was thoroughly are many-faceted mental processes. We believe that detailed by Buzsáki, Logothetis and Singer (2013) and any of the listed points may be corrupted and may im- was mentioned previously: During the evolution of the pair these processes. These corruptions have different mammalian brain, brain wave frequencies and their lin- origins and can be cumulative, impairing the handling ear spacing on a natural logarithmic scale from 0.03 Hz of high-complexity cognitive problems first, partially to 300 Hz remained approximately the same. Coor- due to the lack of components that could simplify the dinated activities, at least for a subset of neuronal ac- task and partially due to the impaired manipulation of tivities across the whole brain, seem critical despite the existing components, including control actions. the relative sizes of different areas and the enormous We argue that social behavior is one of the most growth of the whole brain. involved tasks since it concerns at least one individ- ual other than the self, which increases the number of variables, including the number of hidden variables 3.1 Comorbid disorders and has time constraints in analyzing, making deci- Here we review some typical comorbid disorders. sions and controlling the situations. Consequently, we view autism as the tip of the iceberg of the cumulating problems of component formation and exploitation. 3.1.1 Epilepsy In the following, we shall review comorbidities and Epilepsy is a neurological condition. It is often comor- try to pinpoint to their underlying causes. Afterward, bid with other neurologic and psychiatric disorders. we review the theories developed to explain autism Clinical overlap with ASD is high, and there are many and their strengths and weaknesses. We will then re- comorbid profiles. The prevalences of epilepsy in males late these theories to our model, which relies on the and females are approximately 18% and 34%, respec- complexity of component formation and exploitation. tively (Amiet et al., 2008). There are many candidate genes that could be responsible for the comorbidity; while they are mostly involved in the formation, remod- 3 ‘Results’: Falsifying issues eling and maintenance of synapses and neurotransmis- sion, there are also other dependencies (Lo-Castro and The very first observation is that the list of potential Curatolo, 2014). impairments that may contribute to autism - that is, to Epilepsy is considered a network problem (Kanner, a ‘single’ behavioral problem - is extremely broad and 1943). As such, it is fragile in many ways, including

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(a) Bag Model (b) Spatio-Temporal Model t1 t t+1 motion of (1) right hand (2) body (3) right foot (4) left foot bag of hands bag of feet bag of bodies bottom-up bottom-up processing processing at time t at time t-1 and t top-down predicted end of full body ”right uppercut” motion

time t t1 t t+1

Figure 3: Autoencoding principles. There are two model types, depicted in (a) and (b). The figures serve illustrative purposes. Notations: lower level represents the input. Upper level shows the representation. Body parts at the level of representation (light blue clusters) represent indices, or neurons in the representation and the corresponding top-down vectors that should reconstruct the input and are excited via bottom-up processing. (a): The bag model: Input is broken down into components. Only a few elements of the representation correspond to the input, and thus, representation becomes sparse. (b) The predictive spatio-temporal model: Upon bottom-up processing, the spatio-temporal model propagates in time and predicts – with some uncertainty – the input. The estimated input can be compared with the actual input if propagation can compensate for the delays in bottom-up, top-down and model computations. Error in the delays can tune delay lines. Error in the prediction can tune the predictive model. The interested reader may wish to consult the literature cited in the paper for details of the mathematics involved. Image source: https://openclipart.org/search/?query=stick±man

network centrality (Balardin et al., 2015), a potentially indicates that the genes involved in comorbid disor- serious bottleneck for coordinated activities. Seizures ders have undergone more purifying selection than the may be due to high excitation/inhibition ratios, which genes unique to ASD. are also one of the main theoretical routes proposed in Since there are many interdependencies between autism models (Rubenstein and Merzenich, 2003). In autism and epilepsy, it is difficult to pinpoint to any turn, a high degree of genetic overlap can be expected, specific component-forming mechanism. An important and indeed, ASD and epileptic encephalopathy seem exception is cognitive impairment, which is the most to have many common genetic causes (Srivastava and common outcome of epilepsy; epilepsy can cause con- Sahin, 2017). Restricting particular features, autism- siderable harm to the developing brain (Holmes, 2016). epilepsy phenotypes with macrocephaly may lead to The main connection in our model is that epilepsy gives the separation of relevant subtypes and to improved ge- rise to morphological and physiological changes, modi- netic screening, as suggested by Marchese et al. (2014). fied synaptogenesis and altered excitatory and/or in- The co-occurrence of interictal electroencephalogram hibitory balance, which destroy both network structure abnormalities, regression, and macrocephaly was sug- and dynamics and increase the severity of the compo- gested by Valvo et al. as a distinct endophenotype of nent formation impairment. A falsifying issue is the ASD (Valvo et al., 2016). Another connection is sug- type of impairments that may occur in mesial temporal gested by Bozzi et al. (2016): An excitation-inhibition lobe epilepsy, i.e., epilepsy at the core structure for ratio imbalance resulting from neurodevelopmental forming the episodic and semantic memories that we deficits may be the common pathogenic mechanism, consider the key to understanding autism. According and this hypothesis seems to be supported by genome- to novel findings (Okruszek et al., 2017), mesial tem- wide association studies. However, according to Dickin- poral lobe epilepsy gives rise to social cognitive deficits, son et al. (2016), claims regarding the net increase in including significant mentalizing deficits; these find- excitation or inhibition in autism have little support, if ings support our proposal. any. Inflammation has also been suggested as another Further indications come from brain waves: (i) Sharp route for the neurobehavioral comorbidity of epilepsy wave ripples may undergo pathological conversions and ASD (Mazarati, Lewis, and Pittman, 2017). called ‘p-ripples’, which are the marker of epileptogenic Importantly, there are many metabolic and regula- tissue (Buzsáki, 2015); (ii) Gelinas et al. (2016)ed tory pathways underlying autism, some of which seem memory consolidation, offering a more direct route to unique to this condition (David et al., 2016a). The the impairment of component formation and thus the quest to discover causality relations, including the nec- comorbidity of ASD and epilepsy. Furthermore, the essary and sufficient conditions, is difficult. However, rat model of epilepsy shows that interic- the cited genetic study (Srivastava and Sahin, 2017) tal epileptiform discharges are precisely coordinated

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with spindle oscillations in the prefrontal cortex during brain waves and the animal models related to the work- non-REM sleep and that similar correlations occur in ing of the entorhinal-hippocampal loop and the mem- subjects with focal epilepsy (Gelinas et al., 2016). ory consolidation processes. The abovementioned ‘p-ripples’ can also be observed As previously mentioned, evolution has found a com- in models of schizophrenia (see, e.g., (Buzsáki, plex solution for learning and consolidating new mem- 2015) and the references therein), which is the subject ories in . The solution applies a hierarchy of of the next subsection. brain waves that has undergone only slight variations during evolution despite the orders of magnitude of 3.1.2 Schizophrenia increases in brain weight (Buzsáki and Watson, 2012; Buzsáki, Logothetis, and Singer, 2013). Waves include Definitions of ASD and schizophrenia have changed slow waves, theta, alpha, and gamma waves, spindles over time. In approximately 1950, ASD, which was first and ripples. This seems to represent a delicate coordi- described by Kanner (1943) and Asperger (1944), was nation of machinery that may induce comodulation of considered an early version of schizophrenia (Bender, the power of faster oscillations in both connected and 1947) or a central feature of schizophrenia (Bleuler, non-connected areas and regions (Buzsáki and Wat- 1950). The community started to consider them dif- son, 2012). In schizophrenia, the meticulous design of ferent disorders in approximately 1970 (Rutter, 1972), brain wave hierarchy and the process of coupling brain since ASD starts during childhood and is characterized waves seem to be impaired in diverse ways (Uhlhaas by deficits in social interaction and communication, and Singer, 2006; Uhlhaas et al., 2006; Uhlhaas and whereas schizophrenia typically has a later onset and Singer, 2012). is characterized by psychotic symptoms. There are many constituents involved in manipulat- However, the early negative symptoms of schizophre- ing and forming memories. For example, the power of nia, such as odd behavior and social withdrawal, com- beta and gamma waves increases, and synchronization munication problems, restricted speech, and poor emo- enhances during the maintenance of , tional interactions, are very similar to those of ASD. focus of attention processes, perceptual closure, and Furthermore, there are genetic links between ASD and feature binding, as reviewed by Buzsáki et al. (2013). schizophrenia; see, e.g., (Pina-Camacho, Parellada, However, it has been found that induced gamma os- and Kyriakopoulos, 2016) and the references therein. cillations are reduced in schizophrenic patients and, Regarding the prominent features of schizophrenia, to some extent, in their non-schizophrenic siblings, it appears to affect both men and women at similar too (Grützner et al., 2013). Some reasons underly- rates and has positive and negative symptoms. Posi- ing the problems with gamma waves are related to the tive symptoms include fragmented perception, erro- downregulation of GABA-synthesizing enzymes (Lewis, neous binding of features, delusions, hallucinations, Hashimoto, and Volk, 2005), NMDA receptor hypofunc- and racing thoughts. Negative symptoms, such as ap- tion (Javitt, 2009), and interactions between NMDA athy, blunted responses, reduced speech, social with- receptors and gamma waves(Hong et al., 2010). drawal, and others, may also appear. Other oscillations, such as sleep spindles and theta Different proposals have attempted to connect or sep- and gamma coupling, are also affected in schizophre- arate ASD and schizophrenia. For example, Chisholm nia patients, as shown by (Ferrarelli et al., 2010) and et al. (2015) list diverse models that try to explain (Lisman and Buzsáki, 2008), respectively. Brain wave the frequent co-occurrence of ASD and schizophrenia, imprecisions across large distances may arise from pe- including (i) the multiformity model, which suggests a culiarities and changes in network structure (Cocchi similar underlying disorder but with different expres- et al., 2017) and/or in the speed of information propa- sions; (ii) the increased vulnerability model, which gation, which is determined by axon caliber and myeli- suggests that having one of the impairments increases nation; see, e.g., (Innocenti, Vercelli, and Caminiti, the likelihood of developing the other; (iii) the model 2013) and the references therein. We shall address assuming that ASD and schizophrenia are different these points later. stages of the same disorder; and (iv) the independence We end this subsection with a few notes regard- model, which proposes that when the two disorders ing similarities and dissimilarities in binding and low- co-occur, a third distinct nosological condition actually complexity component formation, respectively, in rela- exists (Larson et al., 2017). tion to the self in ASD and schizophrenia. Brain waves are also considered the underlying rea- son for schizophrenia. In this case, the autoencoder 1. Binding problems have been observed in ASD, e.g., model of component formation requires precise syn- in pairing audio and visual signals and in binding chronization via delay compensation and model-based interoceptive signals (Noel et al., 2018). Binding prediction. When brain wave-driven synchronization requires precise temporal windows, but experi- is corrupted, learning may be considerably impaired. ments with ASD patients show expanded audio- Thus, imprecisions in brain waves may deteriorate com- visual temporal binding windows and completely ponent learning and thus may produce ASD symptoms. diminished temporal acuity for perceiving cardio- We shall examine impairments in the concordance of visual (interoceptive to exteroceptive) information

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(Noel et al., 2018). differences are further emphasized in the emotional 2. Errors in binding give rise to errors in predictive dimension, which is critical in ASD. ADHD and ASD model learning. Self-tickling is an example. The deviate in terms of narrative production in children; insensitivity to self-tickling should not be surpris- children with ASD performing worse on narrative pro- ing given a precise model of the self, and indeed, duction, whereas children with ADHD perform better typical individuals cannot tickle themselves. This than typically developing (TD) children in terms of remains the case even if bodies are swapped in their mean Z-scores for emotional and cognitive terms the body transfer illusion (Van Doorn, Hohwy, and in lexical semantics. In contrast, children with ASD Symmons, 2014). However, the perceptual conse- and ADHD are similar in some ways; e.g., in measures quences of self-produced touch are more ticklish of mean length of utterance, they both perform poorly in individuals with Asperger’s syndrome than in compared with TD children (Kuijper et al., 2017). normal subjects (Blakemore et al., 2006), and An apparent difference is that focus-of-attention is self-tickling is particularly successful for individu- a top-down constraint on bottom-up information flow, als with pronounced schizotypal traits (Lemaitre, and top-down accounts – according to Robertson and Luyat, and Lafargue, 2016). Baron-Cohen (2017) – are not ‘immediately compati- 3. Experimenting with the fast malleability of the ble’ with the empirical findings on ASD, since these find- body representation into the environment, as in ings primarily concern changes in low-level, bottom-up the rubber hand illusion, Noel et al. (2017) found sensory processing. that patients with schizophrenia and ASD behave Our model may resolve this apparent dichotomy be- very differently. Schizophrenia patients had a tween the high ASD-ADHD comorbidity and bottom-up weak or variable bodily boundary between the self (ASD) and top-down (ADHD) characteristics: Compo- and the environment, whereas ASD patients had nent formation involves the autoencoder (Lőrincz and a sharp boundary. We note that the complexity of Buzsáki, 2000; Lőrincz and Sárkány, 2017) and re- the separation of the self from the environment quires bottom-up and recurrent collateral and, finally, corresponds to the separation of self-controlled top-down processing. In turn, problems with top-down components from the rest of the world; thus, processing may involve impairments in component for- such separation seems relatively simple among mation and, in turn, impairments in bottom-up com- the problems of component learning. In turn, putations. In any autoencoder model, the success of for ASD patients, much less impairment is ex- top-down processing depends on the efficiency of mem- pected in this low-complexity task compared with ory consolidation, which is a prerequisite for successful higher-complexity tasks. However, since learn- bottom-up processing since top-down encoding is the ing is more focused on the self than on partners, first step in sparse representations and is followed by learned boundaries may be more rigid for individ- sparse representation-supervised fast bottom-up esti- uals with ASD. mations (Sprechmann, Bronstein, and Sapiro, 2015; Lőrincz et al., 2016). In turn, ADHD impairments of These findings support our proposal that the impair- top-down processing may contribute to ASD symptoms. ments of ASD manifest in high-complexity cognitive New results detailed below support this argument. tasks. In particular, binding can be impaired, but sepa- According to a recent review (Ramtekkar, 2017), ration of low-complexity components seems less sensi- there are considerable similarities between the sleep tive in some cases and it may be spared. problems experienced in ADHD and ASD and in the memory consolidation problems in both disorders. The 3.1.3 Attention deficit, hyperactivity disorder increased REM sleep latency present both in ADHD and ASD (see (Ramtekkar, 2017) and the cited references Attention deficit hyperactivity disorder (ADHD) has the therein) is one of many similarities, and REM sleep has highest incidence rate among all neurodevelopmental been considered important for memory consolidation disorders; it affects >10% of the children in the USA (Siegel, 2005; Diekelmann and Born, 2010). However, (Duda et al., 2017). ADHD and ASD were mutually its direct role has been debated (Stickgold and Walker, exclusive in the Diagnostic and Statistical Manual of 2005). Recently, the picture has become clearer: Mental Disorders [4th ed.; DSM-4; American Psychi- atric Association, 1994]), and simultaneous diagnosis (1) Rasch et al. (2007) provided evidence of was excluded. This has changed in the DSM-5 due the causal role of memory consolidation and to the high comorbidity ratio. However, they differ; hippocampus-driven neocortical reactivation dur- deficits in attention, impulsivity and high activity levels ing sleep, but characterize ADHD and are is independent from social (2) reactivation was observed mostly during slow- context, whereas social communication and interaction wave (SW) sleep and not during REM sleep; see are impaired in many ways in ASD. (Born and Wilhelm, 2012) and the cited references These are considerable differences between the two therein, and disorders, and their comorbidity seems hard to ex- (3) SW and REM sleep have complementary roles in plain. We argue that this is not the case, although the emotional memory consolidation (Cairney et al.,

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2014), whereas autism involve a falsifying issue on environmental ef- (4) REM sleep theta rhythms seem necessary for con- fects beyond genetic causes since component learning textual memory consolidation (Boyce et al., 2016). is inherently connected to experiences and related suc- cesses and failures. Due to the numerous constituents In turn, the consolidation of contextual memory for- of component formation and manipulation, such as mation can be impaired in many ways, including when the speed of processing, the growth features of the SW sleep is reduced (which occurs in ADHD but not network, the sparseness of the neural activities, and in ASD (Ramtekkar, 2017)); when REM sleep latency the interplay between the neocortex and connected is increased, which occurs in both ADHD and ASD structures, including the amygdala, hippocampus, the (Ramtekkar, 2017); and when REM sleep theta rhythm striatum and the cerebellum, one expects considerable is impaired (Boyce et al., 2016). environmental variation, especially among newborns Contextual memory consolidation is critical for com- with different life conditions. ponent learning since the context disambiguates the As detailed by Mandy and Lai (2016), gene- meaning of the component (Fig. 2). In turn, we be- environment interactions and gene-environment corre- lieve that ADHD can be accompanied by ASD due to lations affect ASD risks in many ways as early as the top-down memory consolidation problems. This as- prenatal period; additionally, there are strong postna- pect of autism may be well approximated by Happé tal factors, including caregiver-infant interactions and and Frith’s weak central coherence (WCC) hypothesis deprivation, that can also increase the risk of ASD. (Happé and Frith, 2006), although the full story seems The findings of Mandy and Lai (2016) are supported slightly more complex, since WCC is concerned with by research involving discordant twins, both monozy- impairments in component manipulation, an ADHD gotic and dizygotic. Their development can be influ- like problem, but component formation is a prereq- enced by obstetric factors, first-year infections, and uisite for proper manipulations. In turn, we propose other events (Willfors et al., 2017). Significant effects that the consolidation and exploitation of components can be caused by early medical events, and dysreg- comprise the link between ADHD and ASD. ulations, including feeding and sleep abnormalities, crying, and worriedness. Among the many candidates, the strict ratio of the frequencies of brain oscillations, 3.1.4 Synesthesia the large size of the human brain, and the need for com- Literature on the co-occurrence of autism and synes- munication and synchronization among brain regions thesia is limited. Baron-Cohen et al. (2013) report that highlight the environmental effects on (i) myelination the incidence of synesthesia, in which a sensation in (Forbes and Gallo, 2017), (ii) the connected gyrifica- one modality involves perception in another one, is ap- tion processes (Kates, Ikuta, and Burnette, 2009; Ecker proximately three times higher in autistic adults than et al., 2016; Yoshida et al., 2017), (iii) potential in- in normal subjects. Hughes et al. (2017) found that creases in brain volume (Hazlett et al., 2017), and synesthesia in autism is linked to savant skills. These re- (iv) the developmental trajectories of frontal networks sults are further supported by Ward et al. (2017), who (Catani et al., 2016). Such interactions are complex; found that synesthetes have enhanced perception and their effect may depend strongly on other vulnerabil- attention and exhibit autistic-like impairments, too. It ities as emphasized by Beauchaine and Constantino has been found that axonal connections between V4, (2017), and they all can influence component learning which is involved in color processing, and the so-called and manipulation. ‘grapheme area’ are denser in synesthetes than in con- trols (Rouw and Scholte, 2007). Both of these areas are in the fusiform gyrus, and some portions can be ad- 3.3 Implicit learning, control, and proce- jacent. Other findings support a cross-activation model dural learning (Ramachandran and Seckel, 2015) between these ar- eas, and some of these cross-activations seem to be According to many models, including (Burgess, Recce, preconscious (Ramachandran and Seckel, 2015). In and O’Keefe, 1994; Lőrincz and Buzsáki, 2000; Franz- turn, the comorbidity of autism and synesthesia seems ius, Sprekeler, and Wiskott, 2007; Lőrincz and Szirtes, to arise from network effects, possibly as a result of 2009; Lőrincz and Sárkány, 2017; Finnegan, Shaw, increased axial diffusivity, which can be fostered by and Becker, 2017), among others, one of the tasks of enhanced perception and attention to either colors or the hippocampus is the discovery of components. Fur- graphemes. thermore, and importantly, learned components are to be ‘wired into’ the neocortical substrate as declara- tive (explicit) memory. Declarative memory has two 3.2 Environmental effects: Discordant forms: Semantic memory and episodic, or autobio- monozygotic twins graphic, memory, which allow the individual to learn and recall experiences in the context of space and time. Our assumption that component learning and compo- If component learning is corrupted, then explicit nent exploitation are the underlying impairments in memory can be impaired in diverse ways, including

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the encoding and retrieval processes. However, under and Solomon, 2016) and their cited references. Accord- our model assumptions, implicit memory does not have ing to Cook et al. (2014), facial aftereffects related to be involved, at least not directly. It is then a falsifying to identity and facial expressions are intact in high- issue to determine whether implicit memory has any functioning adults with autism. Since the literature specificity for autistic individuals. The prediction of our presents different results and since the aftereffect is model is that implicit memory is greatly independent weaker in autistic children than in normal subjects, the from explicit memory. authors note that such changes may not be a lifelong Experimental findings indicate that implicit learning feature of the condition (2014) but could be due to is indeed intact in ASD (Renner, Klinger, and Klinger, slowed learning. We shall return to the issue of slow 2000; Brown et al., 2010; Németh et al., 2010; Ring, learning later. Gaigg, and Bowler, 2015). We note the following: In the alternating serial reaction time task, a proce- dural learning task, children with grammar learning (1) For a full account, age-related changes should also deficits were compared with typically developing chil- be considered (Zwart et al., 2017). dren (Hedenius et al., 2011). Both groups showed (2) There are some conflicting findings in ASD individ- evidence of initial learning, but consolidation occurred uals regarding statistical learning, which operates only in the typically developing children. The grammar- mostly implicitly. Arciuli (2017) suggested that impaired group appeared to lose previously gained divergent findings be resolved by considering the knowledge of sequences, and there was a correlation multi-functional needs of statistical learning. Ar- between grammar-related knowledge and procedural ciuli’s arguments exploit the studies by Brown et learning. We propose the following explanation: Chil- al. (2010) that indicate that the degree of com- dren’s grammar-related knowledge can be viewed as munication impairment is independent from per- a compressed structure comprising many individual formance on implicit learning tasks, in contrast examples, and such compressed explicit knowledge with group-level comparisons with different age may alleviate the consolidation process as opposed to groups. the consolidation of individual examples separately. Statistical learn- ing is accompanied by different phenomena, including 3.4 Slowed learning of components repetition enhancement/suppression and aftereffects. Repetition enhancement and suppression have been The speed of learning in declarative memory, including observed after repeated presentations of the stimulus; semantic and episodic memory, is another falsifying neural responses change with repetition. Since repe- issue for us. Our model predicts that component learn- tition suppression and repetition enhancement occur ing is impaired and thus learning is slower and this within the same region (De Gardelle et al., 2012), it is more so for more complex tasks. We first describe has been hypothesized that repetition promotes pre- a specific case related to component formation and dicted activities and suppresses prediction errors, and then present a more general discussion of episodes and the former are sparse. This is consistent with the sparse related communication. coding principles that we emphasize in our model, in The case of Nadia is a very special and revealing which error suppression and representation enhance- example. Nadia was an autistic savant whose horse ment accelerate computation. According to Ewbank et drawings during early childhood were highly artistic al. (2014), repetition suppression is weaker for individ- (Selfe, 1977). These drawings were somewhat similar uals with greater autistic impairment. The intriguing to Leonardo da Vinci’s horse drawings, as shown in finding is that repetition suppression for faces also di- Figs. 1.4 and 1.5 in (Selfe, 2012). Drawings provide minishes if faces are remembered as opposed if they special information; in contrast with Nadia’s drawing, are forgotten (Xue et al., 2011), raising the questions a typical child’s drawings emphasize the components of whether (i) learning is faster or (ii) the effect is and present prototypical views of those components weaker and learning is slower, or (iii) both cases may (Snyder and Thomas, 2001). Nadia’s drawings are occur in autistic individuals. more like photographs; in them, it is difficult to tell The aftereffect is distinguished from repetition sup- whether the horse has eyes or how many legs the horse pression; it is characterized as a behavioral response has, and irrelevant and uncompleted details are added. related to classification. For example, face aftereffects However, by the age of 20, Nadia could draw childish occur upon exposure to an adaptor face that causes drawings! For an example, see Fig. 2.46 in (Selfe, decisions to move towards the opposite characteristics; 2012). It seems that for Nadia, component learning e.g., upon adaptation to a wider face, a normal face ap- was active, but it was very slow compared with that of pears narrower, or if the adaptor face has strong male typically developing children. characteristics, a neutral face will be judged more as Language is a very special part of cognition. By def- a female; see, e.g., (Kovács et al., 2007) and the cited inition, it serves as a compressed representation of references therein. There are many similar findings, episodes, facts, and rules. Words or even sentences are including facial expressions, among others; see, e.g., typically ambiguous, and even when sentences have (Kovács et al., 2007; Hills, 2013; Morgan, Schreiber, perfect syntax, they may not make sense if the con-

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text is not known to a member of the conversation; show deficits in perspective taking (David, Newen, consider Fig.2, for example. The context can come and Vogeley, 2008). from previous sentences or joint experiences, or the sentence may refer to an actual environmental situa- We should add that the sense of agency is less im- tion not known to a remote listener. The distributional paired than the proper model of the self. The for- hypothesis in linguistics – that words that occur in the mer can be intact while the latter can be impaired same context tend to support similar meanings (Harris, in schizophrenia and in ASD, as discussed previously 1954) – shows the relevance of context. In addition in the example of self-tickling. In turn, the self itself is to its episodic nature, language uses components and a sophisticated component, especially in interactions creates a combinatorial set of potential expressions. with partners that have latent variables. In turn, any component may need other components to specify its meaning. Thus, a complete utterance with the same intonation should have the same con- 4 Discussion: Comparisons sequences in the same context. In turn, one does not with other models need to know the compositional nature of language to use a specific utterance for a specific purpose in a In their review, Robertson and Baron-Cohen (2017) specific context. Such repetitions take advantage of lan- ask whether it is time to abandon the idea of a uni- guage, but only as signals and not as a compositional fied account of autism. We put forth a model in this combinatorial structure. paper and say, ‘No’. Here, we discuss why. The model An example of the simplification of the combinato- is supported by recent advances in neuroscience and rial nature of language is if in a dialog, the speaker artificial neural networks. It is based on the following refers to her- or himself in third person, especially if ideas: the third-person reference is primed by the previous sentence and/or by previous dialog. In such cases, the • autism is rooted in a collection of impairments, third person version may come more easily than a first- • the actual collection of impairments may differ for person one. The case that somebody refers to himself each impaired individual, in the third person is frequent in autism. • the common feature of these collections is that elements of them are related to the cornerstone of intelligence, the curse of dimensionality (Bellman, 3.5 The self as a component 1957) and its consequence, the need for explicit memory in cognitive systems with small memory Our basic assumption is that social interaction is com- spans, plex because it includes a considerable number of com- • diversity of the elements arises from the con- ponents, because the partner(s) components are latent, straints of explicit memory formation in the con- and because the latent components might have strong nectome, a distributed neuronal system with rela- inter-individual correlations, e.g., feelings of happiness, tively large delays, and anger, or fear. Important steps for the proper control of • no other psychiatric problem dominates the im- social behavior are (a) the separation of components pairment. belonging to different participants and (b) the concur- rent and goal-oriented adaptation to or manipulation We provide details below and relate our model to of those components. The separation of the self as a others. We start by noting that our sensory spaces have controllable entity is thus critical since it simplifies the huge dimensions, on the order of million(s), whereas control-related tasks in the interaction: The individual our memory span for cognition is between 5 and 9 needs to predict the partner(s) and then decide how items and thus only a few elements (components) of to act accordingly. the representations at a time can be used for cogni- Some findings are support our hypothesis that im- tion. The better the representation, the smaller the pairment in component learning and manipulation can search problem for cognition, the more it fits within influence predictive capabilities and prediction-based this constraint and the faster the solution is. Behavioral actions during social interactions for people with ASD. feedback can shape the representation for improved For example, performance and can optimize behavioral patterns, in- cluding future objectives and goals. • according to recent studies concerning high- For any individual, there are two basic ways to pro- functioning autistic individuals, the ability to pre- ceed for success: dict the actions of others is impaired, but the per- ception of actions of others is not (Von Der Lühe (1) Choose tasks that you know well and optimize et al., 2016), action series further; gain more practice. This is • high-functioning autistic individuals showed no the direction of optimizing procedural memory. evidence of impairments in the so-called ‘sense of (2) Choose tasks that you are interested in but do not agency’, which refers to the experience of the self know (well) yet. Find solutions, try to simplify as the ‘agent’ executing ‘its’ own actions, but did them, think flexibly about the subtasks, find the

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simplest issue to solve and make these solutions capabilities. robust so that they can address different situa- There are a number of ‘algorithms’ required by intact tions. component formation and manipulation. We listed some previously (at the end of Section 2) and now Now, the dilemma is as follows: The tasks that we consider them in this discussion. We proceed as follows: solve depend on us and on the environment. We pri- We consider different theories of autism and address oritize the resources for solving emerging concurrent their strengths and weaknesses from the point of view tasks. The order of the tasks influences the collection of experimental findings and considerations regarding of experiences and thus the learning from errors, and, component formation and manipulations. depending on the tasks, we may become more knowl- edgeable or more precise, and possibly both. Practice 4.1 ASD models: Strengths and weak- requires time, and improvements can be measured. Note, however, that many cognitive problems differ nesses since they are either solved or not and there is no re- There are many models of autism that have both theo- lated simple metric, consider chess, for example. This retical and experimental supports. Some of them are is not the case for mental models about dynamical sys- more involved in information theory and are supported tems, like the body, or the environment. Searches in by diverse experimental findings, like the Bayesian the cognitive space can be impossible or very hard de- Prior Theory. Other models have more heuristic fla- pending on the complexity of the problem that can be vor, like Weak Central Coherence Theory. However, simplified by novel concepts if those are understood. another set of models, such as Inflammation Theory, is For example, prediction in the Ptolemaic model is dif- based on specific, direct experimental findings that try ficult, and a different coordinate system needs to be to derive ASD symptoms as consequences. We review discovered and Newton’s Laws should be mastered to and discuss such models in this subsection. We also practice predictions with high precision. In this regard, comment on them from the point of view of our model. it is an intriguing issue that in rats, there is a special mechanism for the learning of diverse coordinate sys- 4.1.1 Noisy brain and excitation/inhibition ratio tems and the related metric (Taube, 2007) and that imbalance mechanism requires head direction cells. Indeed, the head direction cells can provide supervisory informa- Learning is harder in noise since either the arbitrary tion for the formation of both place cells and grid cells noise is also learned and remembered, which nega- (Lőrincz and Sárkány, 2017), supporting high level tively impacts recognition and generalization, or the cognitive tasks, like homing. noise must be filtered, but perfect filtering is a demand- The two ways, i.e., (1) and (2), work best if they ing task and part of the relevant information may be are combined for an efficient description of the task eliminated during filtering. Rubenstein and Merzenich space, to find proper solutions by thinking, to join (2003) suggested that ASD is the result of noise in the elements of the found solutions into controlling the brain. The proposal seems reasonable in a net- ‘macros’, i.e., longer procedures that can be practiced. work using sparse representation, such as the brain. Rules and theorems are somewhat similar to macros. For example, noise arises if sparsification is inefficient, They also require searches and practice, but in the which points to the potential role of GABAergic feed- cognitive space. Until new components and macros forward and feedback inhibitions, as will be discussed are discovered, one is left to explore new solutions later. However, there is both supporting and conflicting or exploit the old ones. When the latter is the main evidence, suggesting that the picture is complex. method for problem solving, behavior seems rigid. Markram et al. (2007) studied the valproic acid Our proposal for a unifying view of autism is that in rat model of autism in their search for a unifying the- autism, both point (1) and point (2) are impaired in ory of autism. Valproic acid is an anticonvulsant and numerous and cumulative ways. This corrupted nature mood stabilizing drug that has been used for epilepsy becomes more visible in the most complex problems and schizophrenia. Markram et al. proposed that hy- that (i) have many variables and (ii) include hidden perfunctionalities in reactivity, plasticity, perception, variables. Social behavior is such a problem; it requires attention, and memory become debilitating in ASD, both the estimation of many components for joint plan- causing social and environmental withdrawal and lock- ning and practice for joint task execution. Without ing the individual into a small repertoire of proven the proper components for social interaction, thinking routines. In other words, the brain is not noisy, but it takes a long time, and decisions become risky when is highly responsive, and strong influences are damp- compared with established old procedures or routines ened by restricted behaviors. Based on their studies, that work in simpler cases. In turn, the essence of our they propose that excitations are too high and have thinking as we mentioned previously is that autism is behavioral consequences. the tip of the iceberg; autism is the symptom of comor- Davis and Plaisted-Grant (2015) argue that ASD bidity of many underlying cumulative impairments in symptoms reflect too little instead of too much neural component formation and component manipulation noise. They argue that (i) the stochastic resonance

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observed in single unit recordings (see the references 4.1.2 Bayesian Prior Theory they cite) can take advantage of additive noise and may give rise to improved detection and discrimination Pellicano and Burr (2012) suggested the use of thresholds; (ii) noise facilitates transitions between Bayesian models to understand autistic information observations, but such transitions can be slow or even processing. According to Pellicano and Burr, differ- missing in binocular rivalry for ASD, as reported in ences lie in the perceptual mechanisms; namely, peo- (Robertson et al., 2013). Furthermore, (iii) given simi- ple with ASD have ‘hypo-priors’ that give rise to unique, lar inputs belonging to the same category, generaliza- highly precise perceptual experiences. Based on this tion between the inputs may become easier if the noise assumption, Pellicano and Burr explain many autistic level is higher, a well-known algorithmic method used characteristics, from sensory processing to non-social in nonlinear denoising autoencoders (Vincent et al., impairments. 2008); however, generalization processes seem ineffi- A recent work by Palmer et al. (2017) summarizes cient in autism (Plaisted, 2000; Plaisted, 2001). the proposal that in autism, sensory information has Dickinson et al. (2016) reviewed the literature on larger weighting than in normal people. Palmer et al. excitation-inhibition balance in ASD and found that im- argue that balance between perception and action may balances are critical and there is supporting evidence be the characteristic difference between people with for such changes, but taken together, the evidence autism and normal people in both social and non-social justifies neither a net increase in excitation nor a net behaviors. Since action and related learning are con- increase in inhibition in autism. David et al. (2016) nected to procedural memory and thus to repetition consider not the strengths but the variability of cor- , repetition suppression and component learn- tical oscillation patterns. They note that searches for ing and manipulation, the complexity of impairments abnormal power spectra provide inconsistent results in and the escalation of problems during development are autism. They emphasize that trial-to-trial variability beyond our consideration. in cortical oscillations form operational noise in neu- There is little doubt that Bayesian inference is diffi- ronal networks that should consistently communicate cult to discount, and one may ask whether this is the between remote areas, and such variations have been strategy applied by the brain. At first glance, the an- found in ASD. swer seems to be yes; see, e.g., (Berkes et al., 2011; Recent studies of infants with ASD led to intrigu- Lee and Mumford, 2003; Robertson and Baron-Cohen, ing novel findings on cascades of network efficiencies 2017) and the cited references therein. (Lewis et al., 2017). Network inefficiencies were found One may wonder what the winning learning and in infants at high risk of later ASD before symptom working strategy should be in a near-deterministic consolidation. Inefficiencies – detected by MRI seed- world when deterministic actions with deterministic based tractography measures of connection length and outcomes are possible and are desired. Experimental strength – were first apparent in low-level sensory pro- findings on endogenous and exogenously modulated cessing as early as the age of 6 months, but only in binocular rivalry (Brascamp et al., 2018) seem to con- short-range cortico-cortical connectivity. Inefficiency tradict simple Bayesian principles, since a Bayesian then spread to higher-level processing, and ASD symp- observer would always pick the higher-probability in- toms appeared. Symptom severity can be predicted by terpretation. On the other hand, the fact that binocu- the inefficiencies measured much earlier in low-level lar rivalry is slowed down in autism seems consistent processing. Lewis et al. suggest that children with ASD with the Bayesian observer assumption. The switching may suffer from diminished synaptic pruning during phenomenon, however, indicates that simple Bayesian early development. In his comments, Winterer (2017) models are incomplete. aimed to connect network inefficiencies to the endoge- nous neuronal noisy brain found in schizophrenia; see, 4.1.3 Mirror Neuron Theory e.g., (Winterer et al., 2004), and (Peterson et al., 2017) as well as the references cited. In sum, endogenous Since the discovery of mirror neurons (Gallese et al., noise and excitation-inhibition imbalance in the con- 1996; Rizzolatti et al., 1996), that react similarly for text of ASD are controversial but seem to be present in goal-oriented self-motions and for similar motions of different cohorts measured in different ways and with others and thus allow estimations of the intentions of different extents of ASD symptoms (Dickinson, Jones, others, scientists have considered that the mirror neu- and Milne, 2016). ron system may be impaired or possibly dysfunctional Regarding component formation and the cognitive in autism (Williams et al., 2001). In other words, it manipulation of components, both noise and hyper- was thought that in typical cases, mirror neurons can functionality can seriously impair them in many ways provide supervisory information for training and for since component formation presupposes generaliza- copying behavioral templates, as in the case of suc- tion abilities and the development of the precise metric cessful models of the learning of dynamic movement for the components, i.e., discrimination capabilities, primitives (Schaal, 2006; Ijspeert et al., 2013; Chen whereas manipulation requires precisely crafted, well- et al., 2015). synchronized means of control. Experiments support this assumption to some extent:

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µ waves are typically blocked or reduced during volun- cognitive strategy can hardly explain low-level sensory tary muscle movement, e.g., when opening or closing processing problems. the hands, regardless of whether the subject makes We note, however, that component formation and the movement or observes someone else making, but manipulation can exploit either holistic recognition they were not blocked even in high-functioning autistic (Van Belle et al., 2010), or ‘recognition-by-components’ children when they monitored someone else’s muscle (Biederman, 1987), or both. The applied strategy movement (Oberman et al., 2005). should be context dependent, and as such, it is re- These findings on µ waves used to be considered stricted if the quality of formed components is poor. compelling (but, in our view, only implicit) evidence of In turn, the presence or absence of components or the problems with the mirror neuron system (Ramachan- quality of the learned components does influence cog- dran and Oberman, 2006). However, doubts have re- nitive strategy via the feedforward estimation of the mained, and contradicting evidence has been found. available sparse components (Sprechmann, Bronstein, For example, (Bird et al., 2007; Sowden et al., 2016) and Sapiro, 2015) and (Lőrincz et al., 2016). In ad- showed that automatic imitation is intact in ASD. Fur- dition, if holistic, i.e., more complex, recognition is thermore, upon separating automatic imitation from impaired, then component-based recognition followed spatial compatibility effects (i.e., separating responses by inferences may become typical that helps to train on the same side and on the opposite side), there the holistic learning process as illustrated in (Lőrincz was no relationship between spatial compatibility and et al., 2018). autism symptom severity, meaning that individuals with ASD exhibited increased (and not decreased) im- 4.1.5 Going deeper: Specific models itations. The phenomenon is called hyperimitation (Bird et al., 2007; Spengler, Bird, and Brass, 2010; 4.1.6 Delayed development in multisensory pro- Sowden et al., 2016; Deschrijver, Wiersema, and Brass, cessing 2017). These findings are supported by evidence that individuals with ASD frequently engage in strong imi- Our proposal that component formation and manip- tative behavior, such as and echopraxia. ulation are corrupted in ASD means that learning is We note that component learning should support immature, but components may improve with time. In the development of hand representations independent other words, if ASD is an impairment of the neural from the owner of the hand to decrease the curse of learning machinery, then special, topic related impair- dimensionality. However, imitation does not require ments may diminish with time. For example, if anxiety this separation. It is possible that the representation and fear do not interfere or prevent social interactions, of the hand works well, so that representation is sep- then social behavior should improve with time. This arated from the representations of other body parts, is different from other types of impairments, such as but the representation does not have to ‘know’ whether color blindness that may not change. Developmental that hand belongs to the self. For example, thinking trajectories can provide support for such thinking. in terms of the where and what pathways, the what A recent paper (Beker, Foxe, and Molholm, 2018) system may know that it is hand, the where system details this direction and suggests remediations. Beker may know where that hand is; however, these or other et al. put forth arguments about multisensory pro- subsystems may not know well whether that region of cessing. They reviewed studies on sensory integration the peripersonal space as defined by Graziano (2006) deficits at different ages that diminished or sometimes belongs to the self or not, or they may know it well, disappeared over time. Decreases in such sensory in- but the fusion of that information is corrupted with tegration deficits have been found for lower-level and respect to the hand. Lacking this piece of information, higher-level speech processing, such as the McGurk imitation becomes similar to repetition, and that might effect, which can be observed for short plosives; see account for the µ wave-related findings. the discovery (McGurk and MacDonald, 1976) and a review (Alsius, Paré, and Munhall, 2017) of the effect 4.1.4 Weak Central Coherence Theory and the related findings on the dependencies of the autism quotient (Frith, 1991; Woynaroski et al., 2013; Weak Central Coherence (WCC) theory has been an Bebko, Schroeder, and Weiss, 2014; Ujiie et al., 2015), insightful model for autism. Happé and Frith (1994) Beker et al. (2018) examined lower- and higher- put forth the idea that autistic behavior is the result of level sensory integration phenomena, from flashes and impairments in extracting global form and meaning. beeps to speech and gesture, and found that most of Later, the model was modified (Happé and Frith, 2006) them support slower development. Delayed multisen- to say that problems might arise from the superiority sory processing can explain several symptoms found of local processing, which is a bias in the processing in ASD, but sensory integration is only a part of com- strategy, and weak coherence may not be the cause but ponent formation. Some aspects of autism are not ad- a symptom of autistic behavior. Robertson and Baron- dressed if one assumes that this type of impairment is Cohen (2017) object to the theory on the grounds the only underlying impairment of ASD. Impairments that it is a top-down mechanism and that bias in the in multisensory processing can be due to impaired

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recognition capabilities in any modality or to impair- Experimental findings concerning minicolumns are ments in delay compensation in the different process- controversial in ASD. While Casanova et al. report de- ing channels. None of these features alone can explain creases in minicolumnar organization in ASD, others why social learning and language are impaired the have also found differences but reported wider and most. However, impairments in component formation not narrower minicolumns relative to controls (McKa- and exploitation predict the slowed learning of multi- vanagh, Buckley, and Chance, 2015). sensory information fusion, and any slowing down in Component formation may offer explanations for learning supports our model. both types of findings based on theoretical grounds. Minicolumns and their DBCs’ tendency to inhibit the pyramidal cells of neighboring minicolumns create an 4.1.7 Role of inflammation ideal substrate for structured sparse representations, a highly efficient machine-learning approach (Bach, Inflammation has been cited in a recent paper 2010) discussed previously. Here, structure means a (Mazarati, Lewis, and Pittman, 2017) as a cause of group of top-down memories of a generative, i.e., au- epilepsy, ASD, and depression. According to Mazarati toencoding, nature. Unique solutions are warranted et al. (2017), (i) inflammatory markers are elevated in by sparsification. Slow operation is a drawback, but these disorders, (ii) these markers may interfere with fast feedforward estimation of the relevant groups can serotoninergic pathways, (iii) weakening of the inflam- speed up operations (Lőrincz et al., 2016). If the num- matory pathways can reduce the symptoms of these ber of groups required for proper autoencoding is larger disorders, and (iv) early provocation of inflammatory than the dimension of the input (if minicolumns are pathways produces a more excitable brain with an ASD- small), then the operation slows down. Depending like phenotype in animal models. Thus, inflammation on other areas, slower operation may corrupt synchro- can impair and slow down component learning and nization between the areas. In the opposite case, i.e., may induce other impairments. However, inflamma- when minicolumns are large, then feedforward selec- tion alone cannot explain why the primary symptom tion can be fast, but overgeneralization may occur, and of ASD is impaired social relations. On the other hand, discrimination capabilities and category formation can inflammation that may intensify autistic features sup- be impaired. In addition, the smaller number of DBCs ports our assumption that autism is the result of the may lead to overexcited representations. Such pro- comorbidity of different impairments that affect com- cesses could severely impair component learning and ponent formation and manipulation. component manipulation. These are testable cases.

4.1.8 The minicolumn hypothesis 4.1.9 Cortical hyperexpansion, brain volume overgrowth, and spatio-temporal con- An intriguing and challenging observation has been straints made by Casanova et al. (2002; 2004; 2006), (Casanova, 2007). They found changes in the mini- Some developmental findings seem relatively general columnar structure of neocortical superficial layers. In in autism. One example is early brain development particular, they found that minicolumns are smaller in (Piven, Elison, and Zylka, 2017). Piven et al. studied the autistic brain. high-risk siblings later diagnosed with autism early in Minicolumns are the smallest functional units of the their first year and performed neuroimaging studies neocortex, according to Mountcastle (see, e.g., (Mount- beyond their first year. The researchers found cortical castle, 1997)), and they differ significantly between ar- area hyperexpansion in the first year. In the second eas and among mammalian species (Yáñez et al., 2005). year, brain volume overgrowth followed and seemed to Their development also involves double-bouquet cells be associated with the emergence of social deficits. The (DBC), the horsetail-like GABAergic neurons of the researchers reported that hyperexpansion of the corti- superficial layers that underwent a shift during mam- cal surface area occurring during the pre-symptomatic malian evolution. It seems that these neurons play period may co-occur with sensorimotor and attentional an important role in minicolumn organization, but deficits (see, (Piven, Elison, and Zylka, 2017) and the mainly in primates (Yáñez et al., 2005). Furthermore, cited references therein). The changes, according to minicolumns seem to increase with increasing brain Piven et al., alter experience-dependent neuronal devel- mass among anthropoid primates. One may wonder opment, and give rise to increased neuronal activities about brain size and minicolumnar variations in ASD. and, eventually, brain volume overgrowth. The final It should also be noted that minicolumns may not have result is that the refinement of neural circuitry is dis- a function at all, according to some proposals in the rupted, and autistic social deficits become apparent literature (Horton and Adams, 2005). However, recent during the second year of life. studies emphasize the need for further comparative The relevance of these findings is shown in another analyses of the structure of minicolumns and their eco- paper by Piven and colleagues (Hazlett et al., 2017) logical, behavioral, and cognitive correlates (Spocter that was mentioned earlier: within high-risk siblings, et al., 2015). information about brain surface area can predict a pos-

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itive diagnosis of autism with predictive and sensitivity appeared. In addition to intellectual disability, re- values of 81% and 88%, respectively. This appears to duced NMDAR signaling is associated with both be an important trajectory of autism development in schizophrenia and autism. high-risk individuals. Other developmental trajecto- 3. Inhibitory neurons regulate learning in neuronal ries within this group and in other groups should be networks. For example, feedforward and feed- identified for a better understanding of this disorder. back inhibition is influenced by somatostatin- Duan et al. (2017) found specific developmental expressing interneurons, giving rise to behavioral characteristics in the longitudinal MR images of healthy state- dependent inhibition and plasticity. In infants taken at around birth, 1 and 2 years of age. turn, these interneurons integrate different sen- Changes differ at the level of the primary folds, which sory information flows. Parvalbumin-expressing flatten out, whereas most of the minor folds become fast-spiking basket cells change their synaptic more convoluted. strengths in synapses targeting the soma of pyra- In terms of component learning and manipulation, midal cells. According to research findings, fast- constraints on ‘wiring length’ are important since there spiking parvalbumin-expressing interneurons par- is no high-functioning connectome without proper ticipate in the maintenance of the excitatory- wiring. Severe constraints may emerge if the surface inhibitory balance. Errors in regulation are due to area grows slightly faster, making flattening harder molecular mechanisms and can be counteracted and thus (i) restricting the room for minor folds, (ii) via different means, as reviewed by Dehorter et al. increasing the length needed for otherwise short-range (2017). connections in the white matter and (iii) eventually 4. NMDAR dysfunction is related to ASD, and ASD increasing the volume of the brain but still restricting symptoms can be influenced by modifying NM- the fast communication permitted by the constraints DAR function in humans; see (Lee, Choi, and Kim, of scale-free small world structures (see, e.g., (Buzsáki 2015). Furthermore, animal models of ASD in- and Moser, 2013; Kaiser, 2017; Levina and Priese- dicate that correcting NMDAR dysfunction may mann, 2017), and the cited references on scale-free have a therapeutic effect on ASD. small world features in the healthy brain). In other 5. The number of parvalbumin-expressing interneu- words, we suspect that gyrification supports the iden- rons in ASD is smaller than normal in the medial tified scale-free small world wiring, and conflict may temporal cortex (Hashemi et al., 2017). arise between learning and normal brain size if it does 6. A low Cl- ion concentration is critical for proper not. GABAergic inhibitory actions. High concentrations of these ions can give rise to excitatory GABA ac- tions and are characteristic of autism and various 4.1.10 Malfunctioning interneurons types of epilepsy, suggesting another ASD-related Parallel with the putative excitation inhibition imbal- comorbidity family that may benefit from special ance in ASD, parvalbumin-expressing and other in- treatments, as suggested by Ben-Ari et al.; see, hibitory interneurons have become of interest. Re- e.g., (Ben-Ari, 2017; Lemonnier et al., 2017) and searchers have found a few subtleties, e.g., in the knock- the cited references. out mouse model of autism: 7. The GABAergic effect changes from excitatory to inhibitory near birth. This shift seems to be 1. Circuit dysfunction, impaired sensory gating and corrupted in autism, and GABAergic dysfunction social disability emerge in conditional knockout might be related to cortical area hyperexpansion mice in which cox10 expression in parvalbumin (Hazlett et al., 2017). Regulation of the shift via cells has been eliminated, giving rise to mitochon- oxytocin treatment has been suggested by Ben-Ari drial dysfunction in these cells without any loss in (2015). the number of cells. The parvalbumin cells could not maintain their firing rates under these condi- The molecular interactions that are responsible for tions, and imbalances in the exhibitory-inhibitory different dysfunctions give rise to numerous impair- ratio occurred. The resulting behavioral alter- ments. However, these dysfunctions cannot explain ations resembled those observed in schizophrenia why and how ASD - that is, an impairment in social and autism. behavior - becomes the leading symptom in some cases. 2. Disrupted developmental N-methyl-D-aspartate- Other general problems, e.g., network subtleties to be receptor (NMDAR) function was modeled in reviewed later, pose similar questions. knockout mice (Gandal et al., 2012), resulting in changes in the excitation-inhibition balance 4.1.11 Cognitive dysfunction due to increased pyramidal cell excitability and selective disruption of parvalbumin-expressing in- Cantio et al. (2016) studied cognitive-level symptoms terneurons. Changes in the signal-to-noise ratio and searched for a universal pattern of cognitive impair- in the gamma band were observed, among other ments in ASD. They found that two such impairments disruptions, and deficits in social preference also - (i) impaired theory of mind, i.e., theorizing about

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the hidden mental states of other people, and (ii) im- Component learning enables the ‘recognition-by- paired executive function, manifested as repetitive and components’ process (Biederman, 1987), which is less stereotyped behaviors, among other characteristics - fragile (but slower) than holistic recognition; see, e.g., predict autism at a rate of up to 75% (50% being a the examples in (Lőrincz et al., 2018). An impaired random association), which is not a very high value. component system forms a bottleneck for the flexible Nonetheless, their results support the idea that the so- responses required in diverse situations in two ways: cial behavior impairments in autistic individuals may (i) practiced solutions are rigid but safe, whereas (ii) arise from small but potentially different subsets of novel combinations are hard to develop due to impre- many potential causes. In the case of impairments in cise component formation. generalization capabilities, e.g., impairments in proto- We reviewed cognitive impairments that can be type formation, modification of the input distribution caused by hippocampal dysfunction. The existence of can help, as we review below. Prototype formation is such impairments is critical to our proposal regarding a kind of generalization, and it is probably needed for component formation in the understanding of autism component learning. since the learning of declarative memory is not possi- The random dot test is a peculiar example. It was ble in the absence of the hippocampus. In addition to developed by Posner et al. (1967) and showed specific learning, long-term storage is also a necessity, and the and different performances for hippocampal subjects connectome can have other features that may increase (Knowlton and Squire, 1993; Squire and Knowlton, the severity of ASD symptoms. 1995) and for Alzheimer’s patients (Kéri et al., 2001; Kéri et al., 2002; Heindel et al., 2013). In these stud- ies, highly distorted versions of the prototype are first 4.1.12 Problems in the connectome presented. Then, the subject is informed that examples ASD seems to have denser connectivities locally and belong to the same group, and s/he is asked to indi- hypoconnectivity globally (Hughes, 2007). Deviations cate whether a new input belongs to the same group. from optimal network structures may give rise to net- The performance of hippocampal patients is intact, work inefficiencies and may corrupt information pro- but they cannot recognize whether an item has been cessing in the brain; see, e.g., (Lewis et al., 2017) and shown before. In patients with Alzheimer’s disease, the the cited references. Lewis et al. emphasize the vicious probability of low-distortion samples being classified circle issue in their study. They studied infants between as ‘belongs to the group’ overcomes that of the pro- the ages of 6 and 12 months with either high or low totype. In the case of autism, Froelich et al. (2012) risks of having autism. They used diffusion measure- found that prototype formation is intact in ASD, but ments and combined them with longitudinal studies. the classification of the highly distorted samples de- They found that the high-risk infants had inefficiencies pends on item ordering, whereas for normal subjects, in regions that process low-level sensory information. classification is not affected by item ordering. Church Since sensory processing of the visual stream takes et al. (2015) added that children with ASD who show a long time (Kovács et al., 1999; Kovács, 2000), one atypical generalization benefit from learning from a would expect that early discrepancies could have long- single prototypical member of a category, but not from lasting effects on learning and that these effects can learning from many examples. These findings suggest accumulate. Problems with low-level processing can that (i) individual examples can be preserved in some arise from bandwidth problems between groups of neu- ASD cohorts, but these examples may not undergo rons, making information transfer inefficient; this can proper generalization, and (ii) changes in the input give rise to synchronization problems and impair multi- distributions can help with learning. modal information processing and integration, among Ring et al. (2017) studied a critical piece of in- other functions. formation for us: Whether the hippocampus is in- These vulnerabilities can be hampered further by volved in ASD. Our proposal predicts that ASD is local neural structural problems, including the matura- sensitive to hippocampal impairments. Ring et al. tion of white matter myelination and axial and radial used structural learning, biconditional-discrimination axonal diffusivities (Deoni et al., 2016; Ecker et al., and transverse patterning tasks to examine potential 2016). Such problems can slow down learning and the hippocampus-related deficits in ASD. Structural learn- consolidation of components. Learning and consolida- ing requires the learning of spatial, temporal or spatio- tion impairments may manifest in diverse ways, which temporal relations, i.e., particular combinations of el- we discuss below. ements/components during experiences, and conse- quently, this type of learning critically depends on the hippocampus; see the references in (Ring et al., 2017). 4.2 Potential side effects of impair- They found that structural learning was impaired in ments of component formation ASD, but other forms of configural learning were not. Since structural learning is known to be hippocam- Since autism – in our view – is a combination of many pus dependent, the authors claim that their findings impairments that influence component formation and confirm hippocampal involvement in ASD. exploitation, one expects epiphenomena to arise from

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the individual (small) corruptions. We provide a few babbling phase, normal infants start to engage selected examples below: in echolalia; they start to repeat phonemes and words learned from others. Echopraxia is another 1. Components of the body should be properly repre- example of an inborn source of repetitive behav- sented to serve the flexibility of motion control and ior. It is common for normal infants, who produce the related synchronization needs. For example, learned gestures and words spontaneously. For neuronal responses in the macaque motor cortex more details on the development of normal infants, have some similarities to the place cells of the hip- see (Pulvermüller, 2018) and the cited references pocampus; they have the flavor of components, as therein. This type of practicing is an efficient learn- shown by Aflalo and Graziano (2007), but for ac- ing method known as ‘learning by doing’ in many tions. Neurons in the motor cortex discretize the disciplines, from psychology to machine learning final position of the hand in the Cartesian space to education. In the control field, the executed around the body independent of the starting po- (potentially random) motion is considered the de- sition. Such neurons are like the place cells of sired motion and becomes a self-generated sample the ‘cognitive map’, but they are task related; they to learn from. Since learning is slowed down in discretize the targeted end positions of the limb in autism, repetitive behavior helps with learning peripersonal space and are invariant to the initial and is rewarding for longer times. Indeed, autis- position. By contrast, hippocampal place fields tic children also repeat speech and gestures, and discretize the actual position and are invariant to the duration of this period of repetition can be the direction of the body. Impairments in compo- greatly prolonged. Nonetheless, different mecha- nent formation can lead to impairments in the in- nisms may be responsible for repetitive behavior. variant properties and in discretization in general It is known, for example, that autism-associated and may lead to imprecise control; that is, to dys- neuroligin-3 mutations commonly impair striatal praxia. According to a recent report, ASD involves circuits and boost repetitive behaviors (Rothwell considerably larger decrement when executing et al., 2014). simultaneous as opposed to serial gestures than 4. Language is component based and combinatorial, decrement found in control subjects (McAuliffe with considerable need for context-based pattern et al., 2017). Furthermore, praxis errors strongly completion and disambiguation; thus, it can be correlate with autism severity and not IQ (Kaur, and is corrupted in many ways in autism. Srinivasan, and Bhat, 2018). 5. The networks involved in reinforcement learn- 2. There are considerable changes in the basal gan- ing are related to several neurocircuitries, such glia, which is responsible for motor skill acquisi- as the prefrontal-subcortical circuits, amygdala, tion, including eye movement, eye-hand coordi- brainstem, and cerebellum (Schuetze et al., 2017). nation, and movement coordination. These gan- Such complex circuitries can be affected, and such glia form inhibitory loops that, upon disinhibi- considerations are beyond the scope of this work. tion, launch motor actions; they also play a role We note, however, that reinforcement learning un- in action inhibition and sequence learning. The dergoes a combinatorial explosion in the number complex inhibitory structure of the basal ganglia of variables, and there is a need for components may be altered in autism since inhibitory neurons that describe the actual situation in the context are often impaired in ASD. Indeed, volumetric of the actual task. Theory calls this ‘factored rein- changes and alterations in cell densities in the forcement learning’, and factors are engineered in basal ganglia nuclei have been found in autism; the applications (Kearns and Koller, 1999; Szita see, e.g., (Subramanian et al., 2017) and the refer- and Lőrincz, 2009). The learning of many factors ences therein. In autism, the basal ganglia operate (here, components), of which a few are sufficient differently; the sizes of the substructures, e.g., the for a given situation (that is, the learning of sparse putamen, , globus pallidus can factor representation) is a not-yet-solved theoreti- change, and the information flow between these cal problem in machine learning. units and to and from the neocortex also changes. 6. The amygdala, a small subcortical structure in- According to suggestions in the literature reviewed volved in the regulation of emotions, has been by Subramanian et al., (2017), repetitive behavior long suspected to play a role in (some aspects of) is related to these structures. We believe that a autistic behavior. Baron-Cohen et al. (2000) de- restricted motor repertoire and stereotypic behav- veloped the amygdala theory of autism based on iors may also originate from impaired component findings that the amygdala showed limited activ- formation. ity during mentalistic inferences from the eyes 3. Repetitive behavior may have some inborn cir- in subjects with ASD compared with healthy vol- cuitry. Consider babbling, for example. It seems unteers. The volume of the amygdala is larger that the initial babbling mechanism is (i) language in children with ASD than in typically develop- independent and (ii) independent from hearing ing children (Nordahl et al., 2012). There are or deafness (Locke, 1995). Near the end of the additional changes in the amygdala (Weir et al.,

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2018), some of which are common in autism and Is social behavior complex? We answer this ques- in schizophrenia and may give rise to excitation- tion as follows: There are few problems that require inhibition ratio imbalance in this structure (Gao the exploitation of different, well-formed components and Penzes, 2015). Since the amygdala plays a at the same time. The harder of these problems re- role in reinforcement learning (Schuetze et al., quire the estimation of hidden and uncertain compo- 2017), its overall effect in ASD could be large. Al- nents. Estimation of mood, capabilities and intentions though amygdala dysfunction can explain a broad of other people belong to this group of problems. In variety of behavioral impairments, it alone cannot turn, diverse impairments of component formation will explain the richness of phenomena that occur in be more striking in such tasks, and in turn, social be- ASD. havior can be seriously affected. 7. The entorhinal-hippocampal loop has been sug- The route of vulnerability combined with the idea gested to be responsible for novelty detection of component formation has promise for explaining (Lőrincz and Buzsáki, 2000) as an extension of the autism. It also presents a challenge for precision comparator model of the hippocampus suggested medicine. The collection of large amounts of data to- by Vinogradova (1970). Learning, according to gether with data mining (see, e.g., Loth et al. (2016)) these models, proceeds by encoding the observed may be a viable solution for identifying the actual novelty if it is behaviorally relevant. The aberrant causes and intervening according the experienced de- precision account of ASD in the predictive coding velopmental trajectories, more so if these data can model suggests enhanced sensitivity to novelty be combined with genetic information (Szatmari et (Lawson, Rees, and Friston, 2014). On the other al., 2007). Such data collection efforts have already hand, any novelty may predict behavioral failure started; see, e.g., (Autism Spectrum Disorders Working if situations are complex, as is the case for social Group of The Psychiatric Genomics Consortium et al., interactions. Such experiences may give rise to 2017) and the associated references. novelty-related fear and, due to reinforcement learning, to the avoidance of such situations, lead- Acknowledgments ing to a lack of practice and to autistic behavior. The author is most grateful for enlightening discus- sions to Professor Latha V Soorya on autistic behavioral 5 Conclusions problems and to Ábel Fóthi on gene related issues. The project has been supported by the European Union, In a recent paper, Beauchaine and Constantino (2017) co-financed by the European Social Fund (EFOP-3.6.3- discuss the vulnerabilities with respect to ADHD, VEKOP-16-2017-00001). autism, and depression, among other disorders. 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