@article {65283, title = {Fundamental law of memory recall.}, journal = {Physical Review Letters}, volume = {124}, year = {2020}, month = {2020 Jan 10}, pages = {018101}, abstract = {

Human memory appears to be fragile and unpredictable. Free recall of random lists of words is a standard paradigm used to probe episodic memory. We proposed an associative search process that can be reduced to a deterministic walk on random graphs defined by the structure of memory representations. The corresponding graph model can be solved analytically, resulting in a novel parameter-free prediction for the average number of memory items recalled (R) out of M items in memory: R=sqrt[3πM/2]. This prediction was verified with a specially designed experimental protocol combining large-scale crowd-sourced free recall and recognition experiments with randomly assembled lists of words or common facts. Our results show that human memory can be described by universal laws derived from first principles.

}, issn = {1079-7114}, doi = {10.1103/PhysRevLett.124.018101}, author = {Naim, Michelangelo and Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {48533, title = {Theta-paced flickering between place-cell maps in the hippocampus: A model based on short-term synaptic plasticity.}, journal = {Hippocampus}, volume = {27}, year = {2017}, month = {2017 May 30}, pages = {959-70}, abstract = {

Hippocampal place cells represent different environments with distinct neural activity patterns. Following an abrupt switch between two familiar configurations of visual cues defining two environments, the hippocampal neural activity pattern switches almost immediately to the corresponding representation. Surprisingly, during a transient period following the switch to the new environment, occasional fast transitions of activity patterns between the representations (flickering) were observed (Jezek et al. 2011). Here we show that an attractor neural network model of place cells with connections endowed with short-term synaptic plasticity can account for this phenomenon. A memory trace of the recent history of network activity is maintained in the state of the synapses, allowing the network to temporarily reactivate the representation of the previous environment in the absence of the corresponding sensory cues. The model predicts that the number of flickering events depends on the amplitude of the ongoing theta rhythm and the distance between the current position of the animal and its position at the time of cue switching. We test these predictions with new analysis of experimental data. These results suggest a potential role of short-term synaptic plasticity in recruiting the activity of different cell assemblies and in shaping hippocampal activity of behaving animals. This article is protected by copyright. All rights reserved.

}, issn = {1098-1063}, doi = {10.1002/hipo.22743}, author = {Mark, Shirley and Romani, Sandro and Jezek, Karel and Tsodyks, Misha} } @article {47382, title = {Practice makes perfect in memory recall.}, journal = {Learning \& Memory (Cold Spring Harbor, N.Y.)}, volume = {23}, year = {2016}, month = {2016 Apr}, pages = {169-73}, abstract = {

A large variability in performance is observed when participants recall briefly presented lists of words. The sources of such variability are not known. Our analysis of a large data set of free recall revealed a small fraction of participants that reached an extremely high performance, including many trials with the recall of complete lists. Moreover, some of them developed a number of consistent input-position-dependent recall strategies, in particular recalling words consecutively ("chaining") or in groups of consecutively presented words ("chunking"). The time course of acquisition and particular choice of positional grouping were variable among participants. Our results show that acquiring positional strategies plays a crucial role in improvement of recall performance.

}, issn = {1549-5485}, doi = {10.1101/lm.041178.115}, author = {Romani, Sandro and Katkov, Mikhail and Tsodyks, Misha} } @article {46283, title = {Effects of long-term representations on free recall of unrelated words.}, journal = {Learning \& Memory}, volume = {22}, year = {2015}, month = {2015 Jan 15}, pages = {101-8}, abstract = {

Human memory stores vast amounts of information. Yet recalling this information is often challenging when specific cues are lacking. Here we consider an associative model of retrieval where each recalled item triggers the recall of the next item based on the similarity between their long-term neuronal representations. The model predicts that different items stored in memory have different probability to be recalled depending on the size of their representation. Moreover, items with high recall probability tend to be recalled earlier and suppress other items. We performed an analysis of a large data set on free recall and found a highly specific pattern of statistical dependencies predicted by the model, in particular negative correlations between the number of words recalled and their average recall probability. Taken together, experimental and modeling results presented here reveal complex interactions between memory items during recall that severely constrain recall capacity.

}, issn = {1549-5485}, doi = {10.1101/lm.035238.114}, author = {Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {47240, title = {Neural Network Model of Memory Retrieval.}, journal = {Frontiers in Computational Neuroscience}, volume = {9}, year = {2015}, month = {2015 Dec 17}, pages = {149}, abstract = {

Human memory can store large amount of information. Nevertheless, recalling is often a challenging task. In a classical free recall paradigm, where participants are asked to repeat a briefly presented list of words, people make mistakes for lists as short as 5 words. We present a model for memory retrieval based on a Hopfield neural network where transition between items are determined by similarities in their long-term memory representations. Meanfield analysis of the model reveals stable states of the network corresponding (1) to single memory representations and (2) intersection between memory representations. We show that oscillating feedback inhibition in the presence of noise induces transitions between these states triggering the retrieval of different memories. The network dynamics qualitatively predicts the distribution of time intervals required to recall new memory items observed in experiments. It shows that items having larger number of neurons in their representation are statistically easier to recall and reveals possible bottlenecks in our ability of retrieving memories. Overall, we propose a neural network model of information retrieval broadly compatible with experimental observations and is consistent with our recent graphical model (Romani et al., 2013).

}, issn = {1662-5188}, doi = {10.3389/fncom.2015.00149}, author = {Recanatesi, Stefano and Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {46285, title = {Short-term plasticity based network model of place cells dynamics.}, journal = {Hippocampus}, volume = {25}, year = {2015}, month = {2015 Jan}, pages = {94-105}, abstract = {

Rodent hippocampus exhibits strikingly different regimes of population activity in different behavioral states. During locomotion, hippocampal activity oscillates at theta frequency (5-12 Hz) and cells fire at specific locations in the environment, the place fields. As the animal runs through a place field, spikes are emitted at progressively earlier phases of the theta cycles. During immobility, hippocampus exhibits sharp irregular bursts of activity, with occasional rapid orderly activation of place cells expressing a possible trajectory of the animal. The mechanisms underlying this rich repertoire of dynamics are still unclear. We developed a novel recurrent network model that accounts for the observed phenomena. We assume that the network stores a map of the environment in its recurrent connections, which are endowed with short-term synaptic depression. We show that the network dynamics exhibits two different regimes that are similar to the experimentally observed population activity states in the hippocampus. The operating regime can be solely controlled by external inputs. Our results suggest that short-term synaptic plasticity is a potential mechanism contributing to shape the population activity in hippocampus.

}, issn = {1098-1063}, doi = {10.1002/hipo.22355}, author = {Romani, Sandro and Tsodyks, Misha} } @article {46286, title = {Continuous attractor network model for conjunctive position-by-velocity tuning of grid cells.}, journal = {PLoS Computational Biology}, volume = {10}, year = {2014}, month = {2014 Apr 17}, pages = {e1003558}, abstract = {

The spatial responses of many of the cells recorded in layer II of rodent medial entorhinal cortex (MEC) show a triangular grid pattern, which appears to provide an accurate population code for animal spatial position. In layer III, V and VI of the rat MEC, grid cells are also selective to head-direction and are modulated by the speed of the animal. Several putative mechanisms of grid-like maps were proposed, including attractor network dynamics, interactions with theta oscillations or single-unit mechanisms such as firing rate adaptation. In this paper, we present a new attractor network model that accounts for the conjunctive position-by-velocity selectivity of grid cells. Our network model is able to perform robust path integration even when the recurrent connections are subject to random perturbations.

}, keywords = {Animals, Models, Theoretical, Movement, Rodentia}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1003558}, author = {Si, Bailu and Romani, Sandro and Tsodyks, Misha} } @article {46284, title = {Word length effect in free recall of randomly assembled word lists.}, journal = {Frontiers in Computational Neuroscience}, volume = {8}, year = {2014}, month = {2014 Oct 14}, pages = {129}, abstract = {

In serial recall experiments, human subjects are requested to retrieve a list of words in the same order as they were presented. In a classical study, participants were reported to recall more words from study lists composed of short words compared to lists of long words, the word length effect. The world length effect was also observed in free recall experiments, where subjects can retrieve the words in any order. Here we analyzed a large dataset from free recall experiments of unrelated words, where short and long words were randomly mixed, and found a seemingly opposite effect: long words are recalled better than the short ones. We show that our recently proposed mechanism of associative retrieval can explain both these observations. Moreover, the direction of the effect depends solely on the way study lists are composed.

}, issn = {1662-5188}, doi = {10.3389/fncom.2014.00129}, author = {Katkov, Mikhail and Romani, Sandro and Tsodyks, Misha} } @article {46299, title = {Scaling laws of associative memory retrieval.}, journal = {Neural Computation}, volume = {25}, year = {2013}, month = {2013 Oct}, pages = {2523-44}, abstract = {

Most people have great difficulty in recalling unrelated items. For example, in free recall experiments, lists of more than a few randomly selected words cannot be accurately repeated. Here we introduce a phenomenological model of memory retrieval inspired by theories of neuronal population coding of information. The model predicts nontrivial scaling behaviors for the mean and standard deviation of the number of recalled words for lists of increasing length. Our results suggest that associative information retrieval is a dominating factor that limits the number of recalled items.

}, keywords = {Algorithms, Association Learning, Computer Simulation, Humans, Memory, Mental Recall, Models, Neurological, Neural Networks (Computer)}, issn = {1530-888X}, doi = {10.1162/NECO_a_00499}, author = {Romani, Sandro and Pinkoviezky, Itai and Rubin, Alon and Tsodyks, Misha} } @article {46300, title = {Intracellular dynamics of virtual place cells.}, journal = {Neural Computation}, volume = {23}, year = {2011}, month = {2011 Mar}, pages = {651-5}, abstract = {

The pattern of spikes recorded from place cells in the rodent hippocampus is strongly modulated by both the spatial location in the environment and the theta rhythm. The phases of the spikes in the theta cycle advance during movement through the place field. Recently intracellular recordings from hippocampal neurons (Harvey, Collman, Dombeck, \& Tank, 2009 ) showed an increase in the amplitude of membrane potential oscillations inside the place field, which was interpreted as evidence that an intracellular mechanism caused phase precession. Here we show that an existing network model of the hippocampus (Tsodyks, Skaggs, Sejnowski, \& McNaughton, 1996 ) can equally reproduce this and other aspects of the intracellular recordings, which suggests that new experiments are needed to distinguish the contributions of intracellular and network mechanisms to phase precession.

}, issn = {1530-888X}, doi = {10.1162/NECO_a_00087}, author = {Romani, Sandro and Sejnowski, Terrence J and Tsodyks, Misha} } @article {46301, title = {Continuous attractors with morphed/correlated maps.}, journal = {PLoS Computational Biology}, volume = {6}, year = {2010}, month = {2010 Aug 5}, pages = {e1000869}, abstract = {

Continuous attractor networks are used to model the storage and representation of analog quantities, such as position of a visual stimulus. The storage of multiple continuous attractors in the same network has previously been studied in the context of self-position coding. Several uncorrelated maps of environments are stored in the synaptic connections, and a position in a given environment is represented by a localized pattern of neural activity in the corresponding map, driven by a spatially tuned input. Here we analyze networks storing a pair of correlated maps, or a morph sequence between two uncorrelated maps. We find a novel state in which the network activity is simultaneously localized in both maps. In this state, a fixed cue presented to the network does not determine uniquely the location of the bump, i.e. the response is unreliable, with neurons not always responding when their preferred input is present. When the tuned input varies smoothly in time, the neuronal responses become reliable and selective for the environment: the subset of neurons responsive to a moving input in one map changes almost completely in the other map. This form of remapping is a non-trivial transformation between the tuned input to the network and the resulting tuning curves of the neurons. The new state of the network could be related to the formation of direction selectivity in one-dimensional environments and hippocampal remapping. The applicability of the model is not confined to self-position representations; we show an instance of the network solving a simple delayed discrimination task.

}, keywords = {Animals, Computer Simulation, Discrimination (Psychology), Hippocampus, Models, Biological, Neural Networks (Computer), Numerical Analysis, Computer-Assisted}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000869}, author = {Romani, Sandro and Tsodyks, Misha} }