From Episodic to Semantic Memory in a Spiking Cortical Network Model
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bioRxiv preprint doi: https://doi.org/10.1101/2021.07.18.452769; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Traces of semantization - from episodic to semantic memory in a spiking cortical network model Nikolaos Chrysanthidis1 · Florian Fiebig1 · Anders Lansner1,2 · Pawel Herman3 1 Abstract Episodic memory is the recollection of past sistance to semantization. Our model reproduces impor- 29 2 personal experiences associated with particular times tant features of episodicity on behavioral timescales un- 30 3 and places. This kind of memory is commonly sub- der various biological constraints whilst also offering 31 4 ject to loss of contextual information or "semantiza- a novel neural and synaptic explanation for semantiza- 32 5 tion", which gradually decouples the encoded memory tion, thereby casting new light on the interplay between 33 6 items from their associated contexts while transforming episodic and semantic memory processes. 34 7 them into semantic or gist-like representations. Novel Keywords Episodic memory · Semantic memory · 35 8 extensions to the classical Remember/Know behavioral Semantization · Decontextualization · Bayesian-Hebbian 36 9 paradigm attribute the loss of episodicity to multiple plasticity · BCPNN · STDP · Spiking cortical memory 37 10 exposures of an item in different contexts. Despite re- model 38 11 cent advancements explaining semantization at a behav- 12 ioral level, the underlying neural mechanisms remain 13 poorly understood. In this study, we suggest and evalu- 1 Introduction 39 14 ate a novel hypothesis proposing that Bayesian-Hebbian 15 synaptic plasticity mechanisms might cause semanti- Remembering single episodes is a fundamental attribute 40 16 zation of episodic memory. We implement a cortical of human cognition. A memory, such as with whom you 41 17 spiking neural network model with a Bayesian-Hebbian celebrated your last birthday, is more vividly recreated 42 18 learning rule called Bayesian Confidence Propagation when we are able to recall contextual information, such 43 19 Neural Network (BCPNN), which captures the semanti- as the location of the event (Eichenbaum et al., 2007; 44 20 zation phenomenon and offers a mechanistic explanation Gillund, 2012). The term "episodic memory" was orig- 45 21 for it. Encoding items across multiple contexts leads to inally introduced by Tulving (1972) to designate mem- 46 22 item-context decoupling akin to semantization. We com- ories of personal experiences. Retrieval from episodic 47 23 pare BCPNN plasticity with the more commonly used memory includes a feeling of mental time travel realized 48 24 spike-timing dependent plasticity (STDP) learning rule in by "I remember". In contrast, semantic memory retrieval 49 25 the same episodic memory task. Unlike BCPNN, STDP encapsulates what is best described by "I know" (Tulving, 50 26 does not explain the decontextualization process. We also 1985; Umanath and Coane, 2020). Unlike episodic mem- 51 27 examine how selective plasticity modulation of isolated ories, semantic memories refer to general knowledge 52 28 salient events may enhance preferential retention and re- about words, items and concepts, lacking spatiotemporal 53 source information, possibly resulting from the accumu- 54 Nikolaos Chrysanthidis lation of episodic memories (Schendan, 2012; Gillund, 55 E-mail: [email protected] 2012). 56 Florian Fiebig Initially, Tulving (1972) proposed that episodic and 57 E-mail: fi[email protected] semantic memory are distinct systems and compete in re- 58 Anders Lansner trieval. Recent studies suggest, however, that the division 59 E-mail: [email protected] between episodic and semantic memory is rather vague 60 Pawel Herman (McCloskey and Santee, 1981; Renoult et al., 2019), 61 E-mail: [email protected] as neural activity reveals interaction between episodic 62 1 School of Electrical Engineering and Computer Science, and semantic systems during retrieval (Weidemann et al., 63 KTH Royal Institute of Technology, 10044 Stockholm, Sweden 2019). According to Squire and Zola (1998) retrieval 64 of semantic memory depends on the acquisition of the 65 2 Department of Mathematics, Stockholm University, 10691 Stockholm, Sweden episode in which such information was experienced. Ap- 66 parently, there is a clear interdependence between the 67 3 School of Electrical Engineering and Computer Science, two systems as the content of episodic memory invari- 68 and Digital Futures, KTH Royal Institute of Technology, 10044 ably involves semantic representations (Martin-Ordas 69 Stockholm, Sweden bioRxiv preprint doi: https://doi.org/10.1101/2021.07.18.452769; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is 2 made available under aCC-BY-NC-ND 4.0 International license. Nikolaos Chrysanthidis1 et al. 70 et al., 2014), and consequently semantic similarity aids formed one-shot memory encoding and was further ex- 130 71 episodic retrieval (Howard and Kahana, 2002). panded into a two-area cortical model used to explore 131 72 Episodic memory traces are susceptible to transfor- a novel indexing theory of working memory, based on 132 73 mation and loss of information (Tulving, 1972), and fast Hebbian synaptic plasticity (Fiebig et al., 2020). In 133 74 this loss of episodicity can be attributed to semantiza- this context, it was suggested that the underlying naive 134 75 tion, which typically takes the form of a decontextual- Bayes view of association would make the associative 135 76 ization process (Duff et al., 2020; Habermas et al., 2013; binding between two items weaker if one of them is later 136 77 Viard et al., 2007). Meeter and Murre (2004) highlight associated with additional items. Thus, if we conceive of 137 78 and review the dynamical nature of memories and neu- episodicity as an associative binding between item and 138 79 ral interactions through the scope of Transformation the- context, the BCPNN synaptic plasticity update rule might 139 80 ory, which suggests that all memories start as episodic provide a mechanism for semantization. In this work, we 140 81 representations that gradually transform into semantic test this hypothesis and examine to what extent the re- 141 82 or gist-like representations (Winocur and Moscovitch, sults match available behavioral data on semantization. 142 83 2011; Petrican et al., 2010). Decontextualization can oc- We further compare those outcomes of dynamic learn- 143 84 cur over time as studies suggest that older adults report ing with a model featuring the more well-known spike- 144 85 fewer episodic elements than younger adults (Petrican timing dependent plasticity (STDP) learning rule. We 145 86 et al., 2010). Yet, could this item-context decoupling rely also demonstrate how selective plasticity modulations of 146 87 on accumulation of episodicity over multiple exposures one-shot learning (tentatively modelling effects of atten- 147 88 of stimuli in various contexts over time? Baddeley (1988) tion, emotional salience, valence, surprise, etc. on plas- 148 89 hypothesized that semantic memory might represent the ticity) may enhance episodicity and counteract semanti- 149 90 accumulated residue of multiple learning episodes, con- zation. 150 91 sisting of information which has been semanticized and To our knowledge, there are no previous compu- 151 92 detached from the associated episodic contextual de- tational models of item-context decoupling akin to se- 152 93 tail. In fact, simple language vocabulary learning implies mantization. On the whole, there are rather few com- 153 94 that learners encode words in several different contexts, putational models of episodic memory (Norman and 154 95 which leads to semantization and definition-like knowl- O’Reilly, 2003), and those that exist are typically ab- 155 96 edge of the studied word (Beheydt, 1987; Bolger et al., stract, aimed at predicting behavioral outcomes without 156 97 2008). a clear focus on underlying neural and synaptic mech- 157 98 Retrieval from episodic memory has been studied anisms (Greve et al., 2010; Wixted, 2007). Our model 158 99 extensively through the lens of the classical Remem- bridges these perspectives and explains semantization 159 100 ber/Know (R/K) paradigm, in which participants are re- based on synaptic plasticity, while it also reproduces im- 160 101 quired to provide a Know or Remember response to portant episodic memory phenomena on behavioral time 161 102 stimulus-cues, judging whether they are able to recall scales under constrained network connectivity with plau- 162 103 item-only information or additional detail about episodic sible postsynaptic potentials, firing rates, and other bio- 163 104 context, respectively (van den Bos et al., 2020). Exten- logical parameters. 164 105 sions of the classical R/K behavioral experiment demon- 106 strate that item-context decoupling can occur rapidly 2 Materials and Methods 165 107 (Opitz, 2010). In these experiments, items are presented 108 during an encoding phase either in a unique context, 2.1 Neuron and synapse model 166 109 or across several contexts. Low context variability leads 110 to greater recollection, whereas context overload results We use adaptive exponential integrate-and-fire point 167 111 in decontextualization and a higher fraction of correctly model neurons, which feature spike frequency adapta- 168 112 classified Know responses (Opitz, 2010; Smith and Man- tion, enriching neural dynamics and spike patterns, espe- 169 113 zano, 2010; Smith and Handy, 2014). In the current cially for the pyramidal cells (Brette and Gerstner, 2005). 170 114 study, we offer and evaluate a Bayesian-hypothesis about The neuron model offers a good phenomenological de- 171 115 synaptic and network mechanisms underlying the mem- scription of typical neural firing behavior, but it is limited 172 116 ory semantization (item-context decoupling).