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      Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State

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          Abstract

          In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subserve diverse representations, from explicit mappings in physical spaces to generalized mappings in the task space, and up to complex abstract transformations such as working memory, decision-making and behavioral planning. Computational models have separately assessed learning and replay of neural trajectories, often using unrealistic learning rules or decoupling simulations for learning from replay. Hence, the question remains open of how neural trajectories are learned, memorized and replayed online, with permanently acting biological plasticity rules. The asynchronous irregular regime characterizing cortical dynamics in awake conditions exerts a major source of disorder that may jeopardize plasticity and replay of locally ordered activity. Here, we show that a recurrent model of local PFC circuitry endowed with realistic synaptic spike timing-dependent plasticity and scaling processes can learn, memorize and replay large-size neural trajectories online under asynchronous irregular dynamics, at regular or fast (sped-up) timescale. Presented trajectories are quickly learned (within seconds) as synaptic engrams in the network, and the model is able to chunk overlapping trajectories presented separately. These trajectory engrams last long-term (dozen hours) and trajectory replays can be triggered over an hour. In turn, we show the conditions under which trajectory engrams and replays preserve asynchronous irregular dynamics in the network. Functionally, spiking activity during trajectory replays at regular timescale accounts for both dynamical coding with temporal tuning in individual neurons, persistent activity at the population level, and large levels of variability consistent with observed cognitive-related PFC dynamics. Together, these results offer a consistent theoretical framework accounting for how neural trajectories can be learned, memorized and replayed in PFC networks circuits to subserve flexible dynamic representations and adaptive behaviors.

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          Most cited references 152

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          Cellular basis of working memory

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            How inhibition shapes cortical activity.

            Cortical processing reflects the interplay of synaptic excitation and synaptic inhibition. Rapidly accumulating evidence is highlighting the crucial role of inhibition in shaping spontaneous and sensory-evoked cortical activity and thus underscores how a better knowledge of inhibitory circuits is necessary for our understanding of cortical function. We discuss current views of how inhibition regulates the function of cortical neurons and point to a number of important open questions. Copyright © 2011 Elsevier Inc. All rights reserved.
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              Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons.

               N Brunel (2015)
              The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically. The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity; and states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than the global oscillation frequency. The network can switch between these states, provided the external frequency, or the balance between excitation and inhibition, is varied. Two types of network oscillations are observed. In the fast oscillatory state, the network frequency is almost fully controlled by the synaptic time scale. In the slow oscillatory state, the network frequency depends mostly on the membrane time constant. Finite size effects in the asynchronous state are also discussed.
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                Author and article information

                Contributors
                Journal
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                1662-5110
                08 July 2021
                2021
                : 15
                Affiliations
                1Institut des Systèmes Intelligents et de Robotique, CNRS, Inserm, Sorbonne Université , Paris, France
                2CEA Paris-Saclay, CNRS, NeuroSpin , Saclay, France
                3Neuroscience Paris Seine - Institut de biologie Paris Seine, CNRS, Inserm, Sorbonne Université , Paris, France
                Author notes

                Edited by: Shintaro Funahashi, Kyoto University, Japan

                Reviewed by: Shantanu P. Jadhav, Brandeis University, United States; Lukas Ian Schmitt, RIKEN Center for Brain Science (CBS), Japan

                *Correspondence: Matthieu X. B. Sarazin matthieu.sarazin@ 123456live.fr

                †These authors have contributed equally to this work

                Article
                10.3389/fncir.2021.648538
                8298038
                Copyright © 2021 Sarazin, Victor, Medernach, Naudé and Delord.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 8, Tables: 0, Equations: 29, References: 152, Pages: 26, Words: 19229
                Categories
                Neuroscience
                Original Research

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