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      When shared concept cells support associations: Theory of overlapping memory engrams

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          Abstract

          Assemblies of neurons, called concepts cells, encode acquired concepts in human Medial Temporal Lobe. Those concept cells that are shared between two assemblies have been hypothesized to encode associations between concepts. Here we test this hypothesis in a computational model of attractor neural networks. We find that for concepts encoded in sparse neural assemblies there is a minimal fraction c min of neurons shared between assemblies below which associations cannot be reliably implemented; and a maximal fraction c max of shared neurons above which single concepts can no longer be retrieved. In the presence of a periodically modulated background signal, such as hippocampal oscillations, recall takes the form of association chains reminiscent of those postulated by theories of free recall of words. Predictions of an iterative overlap-generating model match experimental data on the number of concepts to which a neuron responds.

          Author summary

          Experimental evidence suggests that associations between concepts are encoded in the hippocampus by cells shared between neuronal assemblies (“overlap” of concepts). What is the necessary overlap that ensures a reliable encoding of associations? Under which conditions can associations induce a simultaneous or a chain-like activation of concepts? Our theoretical model shows that the ideal overlap presents a tradeoff: the overlap should be larger than a minimum value in order to reliably encode associations, but lower than a maximum value to prevent loss of individual memories. Our theory explains experimental data from human Medial Temporal Lobe and provides a mechanism for chain-like recall in presence of inhibition, while still allowing for simultaneous recall if inhibition is weak.

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          Most cited references42

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Neurons with graded response have collective computational properties like those of two-state neurons.

            J Hopfield (1984)
            A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons. The content- addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.
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              Invariant visual representation by single neurons in the human brain.

              It takes a fraction of a second to recognize a person or an object even when seen under strikingly different conditions. How such a robust, high-level representation is achieved by neurons in the human brain is still unclear. In monkeys, neurons in the upper stages of the ventral visual pathway respond to complex images such as faces and objects and show some degree of invariance to metric properties such as the stimulus size, position and viewing angle. We have previously shown that neurons in the human medial temporal lobe (MTL) fire selectively to images of faces, animals, objects or scenes. Here we report on a remarkable subset of MTL neurons that are selectively activated by strikingly different pictures of given individuals, landmarks or objects and in some cases even by letter strings with their names. These results suggest an invariant, sparse and explicit code, which might be important in the transformation of complex visual percepts into long-term and more abstract memories.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput Biol
                plos
                PLoS Computational Biology
                Public Library of Science (San Francisco, CA USA )
                1553-734X
                1553-7358
                December 2021
                30 December 2021
                : 17
                : 12
                : e1009691
                Affiliations
                [1 ] School of Computer and Communication Sciences and School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
                [2 ] Institut für Mathematik, Technische Universität Berlin, Berlin, Germany
                [3 ] School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
                [4 ] Centre for Systems Neuroscience, University of Leicester, Leicester, United Kingdom
                [5 ] Peng Cheng Laboratory, Shenzhen, China
                Research Center Jülich, GERMANY
                Author notes

                The authors have declared that no competing interests exist.

                ‡ These authors jointly supervised this work.

                Author information
                https://orcid.org/0000-0002-6089-7652
                https://orcid.org/0000-0002-5422-3723
                https://orcid.org/0000-0002-2275-7229
                https://orcid.org/0000-0002-4344-2189
                Article
                PCOMPBIOL-D-21-00730
                10.1371/journal.pcbi.1009691
                8754331
                34968383
                36bc8593-7ffd-4c29-bed7-b42b0a8ee5a9
                © 2021 Gastaldi et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 April 2021
                : 29 November 2021
                Page count
                Figures: 9, Tables: 0, Pages: 44
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 200020_184615
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 200020_184615
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 785907
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100010661, Horizon 2020 Framework Programme;
                Award ID: 785907
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000268, Biotechnology and Biological Sciences Research Council;
                Award Recipient :
                WG and CG were supported by the Swiss National Science Foundation ( www.nsf.gov), grant agreement 200020_184615 and by the European Union Horizon 2020 Framework Program ( https://ec.europa.eu/programmes/horizon2020/) under agreement no. 785907 (HumanBrain Project, SGA2). RQQ acknowledges support from Biotechnology and Biological Sciences Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Neurons
                Biology and Life Sciences
                Neuroscience
                Cellular Neuroscience
                Neurons
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Memory Recall
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Memory Recall
                Biology and Life Sciences
                Anatomy
                Brain
                Hippocampus
                Medicine and Health Sciences
                Anatomy
                Brain
                Hippocampus
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Mathematical Functions
                Transfer Functions
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Single Neuron Function
                Biology and Life Sciences
                Neuroscience
                Computational Neuroscience
                Single Neuron Function
                Computer and Information Sciences
                Systems Science
                Dynamical Systems
                Physical Sciences
                Mathematics
                Systems Science
                Dynamical Systems
                Custom metadata
                vor-update-to-uncorrected-proof
                2022-01-12
                All relevant data are within the manuscript and its Supporting information files. The code used to numerically solve the equations derived in the manuscript is available at https://github.com/ChiaraGastaldi/pub_Gastaldi_2021_AttractorNetwork.git.

                Quantitative & Systems biology
                Quantitative & Systems biology

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