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Are Place Cells Just Memory Cells? Memory Compression Leads to Spatial Tuning and History Dependence

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Are Place Cells Just Memory Cells? Memory Compression Leads to Spatial Tuning and History Dependence bioRxiv preprint doi: https://doi.org/10.1101/624239; this version posted August 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Are place cells just memory cells? Memory compression leads to spatial tuning and history dependence Marcus K. Bennaa,b,c,1,2 and Stefano Fusia,b,d,1,2 aCenter for Theoretical Neuroscience, Columbia University; bMortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University; cNeurobiology Section, Division of Biological Sciences, University of California, San Diego; dKavli Institute for Brain Sciences, Columbia University This manuscript was compiled on August 31, 2020 The observation of place cells has suggested that the hippocampus to what appears to be noise. Some of the components of plays a special role in encoding spatial information. However, place the variability probably depend on the variables that are cell responses are modulated by several non-spatial variables, and represented at the current time, but some others might depend reported to be rather unstable. Here we propose a memory model also on the recent history, or in other words, they might be of the hippocampus that provides a novel interpretation of place affected by the storage of recent memories. cells consistent with these observations. We hypothesize that the The model of the hippocampus we propose not only pre- hippocampus is a memory device that takes advantage of the corre- dicts that place cells should exhibit history effects, but also lations between sensory experiences to generate compressed rep- that their spatial tuning properties simply reflect an efficient resentations of the episodes that are stored in memory. A simple strategy for storing correlated patterns. Much of the theoreti- neural network model that can efficiently compress information nat- cal work on the memory capacity of neural networks is based urally produces place cells that are similar to those observed in ex- on the assumption that the patterns representing memories are periments. It predicts that the activity of these cells is variable and random and uncorrelated (see e.g. (14–18)). This assumption that the fluctuations of the place fields encode information about the is not unreasonable for long-term storage, despite the fact recent history of sensory experiences. Place cells may simply be a that most of our sensory experiences are highly correlated. consequence of a memory compression process implemented in the Indeed, to efficiently store correlated episodes, it is desirable hippocampus. to preprocess the new memories and compress them before they are placed in long-term memory. Ideally, one would Hippocampus | Place Cells | Memory Models | Compression | Correlated want to extract the uncorrelated (and hence incompressible) Activity Patterns components of the new input patterns and store only those. However, this form of preprocessing and compression, which Introduction would lead to uncorrelated patterns of synaptic modifications, has not been explicitly modeled in previous theoretical studies Several studies show that neurons in the hippocampus encode of memory capacity. the position of the animal in its environment and as a con- We hypothesize that this preprocessing of the incoming sequence they have been named "place cells" (see e.g. (1)). stream of potential episodic memories is to some extent car- Here we propose a novel interpretation of the observation of ried out in the hippocampus, which in itself has long been place cells by suggesting that their response properties ac- tually reflect a process of memory compression in which the hippocampus plays a fundamental role. Our interpretation Significance Statement contributes to the reconciliation between two dominant, but apparently different points of view: one involving the hip- Numerous studies on humans revealed the importance of the pocampus in spatial cognitive maps and navigation, versus hippocampus in memory formation. The rodent literature in- another one that considers the hippocampus playing a broad stead focused on the spatial representations that are observed role in episodic and declarative memory (see e.g. (2, 3)). in navigation experiments. Here we propose a simple model The first view is supported by the observation of place cells, of the hippocampus that reconciles the main findings of the some of which exhibit responses that are easily interpretable as human and rodent studies. The model assumes that the hip- the cells tend to fire only when the animal is in one particular pocampus is a memory system that generates compressed location (single field place cells). However, it is becoming representations of sensory experiences using previously ac- clear that in many brain areas, including the hippocampus quired knowledge about the statistics of the world. These expe- and entorhinal cortex, neural responses are very diverse (4–8), riences can then be memorized more efficiently. The sensory highly variable in time (9, 10) and modulated by multiple experiences during the exploration of an environment, when variables (8, 11–13). Place cells might respond at single or compressed by the hippocampus, lead naturally to spatial rep- multiple locations, and multiple visitations of the same location resentations similar to those observed in rodent studies and to typically elicit different responses. Part of this diversity can be the emergence of place cells. explained by assuming that each neuron responds non-linearly to a different combination of multiple external or internal No conflict of interest declared. variables (mixed selectivity; see e.g. (5, 6)). The variability 1 M.K.B. and S.F. contributed equally to this work. might be due to the fact that some of these variables are 2To whom correspondence should be addressed. E-mail: [email protected] and not being monitored in the experiment, and hence contribute [email protected] 1–13 bioRxiv preprint doi: https://doi.org/10.1101/624239; this version posted August 31, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. considered as a memory storage system, albeit with a longest Results time constant that is shorter than that of cortex (19, 20). New sensory episodes are often similar to old ones and hence their Storing correlated patterns efficiently. Most of the patterns neural representations are likely to be correlated to those of sen- we would like to store in memory are likely to be highly cor- sory experiences that are already in memory. Storing directly related, as our experiences are often similar to each other. the neural representations of these sensory episodes would be Storing correlated patterns in artificial neural networks typi- inefficient because they are redundant. However, since the cally requires a synaptic learning rule that is more complicated hippocampus is highly structured and contains distinct regions than simple Hebbian plasticity to avoid a bias towards the that might implement different stages of processing, it can memory components shared my multiple memories. Simple transform and compress incoming sensory information with extensions such as the perceptron learning rule can already respect to other memories already stored there. An important deal with many forms of correlations. However, storing cor- role of the hippocampus in compressing memories has already related patterns in their original format is rarely the optimal been suggested in (21) (see also the Discussion). strategy and often it is possible to greatly increase the memory capacity by constructing compressed neural representations We have built a concrete neural network model that imple- that explicitly take into account the correlations. This form ments this compression process in a manner broadly consistent of preprocessing can be illustrated with a simple example in with the known neuroanatomy. The model consists of a three- which the patterns to be memorized are organized as in an layer network in which the first layer represents the sensory in- ultrametric tree (see e.g. (24) and Figure 1a). To generate puts (which would already have been processed by the sensory these correlated patterns one starts from p uncorrelated ran- areas corresponding to different modalities). The feed-forward dom patterns (the ancestors at the top of the tree). In these synapses that connect it to the second layer are continuously patterns each neuron is either active or inactive with equal modified to create a compressed, sparse representation of the probability, as in the case of the Hopfield model (14). One can inputs. This layer implements a form of statistical learning, as then generate k descendants for each ancestor by resampling the compressed representations are based on the statistics of randomly with a certain probability 1 − γ the activation state recent sensory experiences (and possibly input from cortical of each of the neurons in the patterns representing the an- long-term memory in the case of familiar environments, as cestors. The parameter k is called the branching ratio of the discussed below). A third layer is used to store the memories, tree. The total number of descendants is p k, and these are the i.e., specific episodes. This architecture and the computational patterns that we intend to store in memory. The descendants principles are similar to those proposed in (22) (see also the that come from the same ancestor will be
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