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      Acetylcholine-gated current translates wake neuronal firing rate information into a spike timing-based code in Non-REM sleep, stabilizing neural network dynamics during memory consolidation

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          Abstract

          Sleep is critical for memory consolidation, although the exact mechanisms mediating this process are unknown. Combining reduced network models and analysis of in vivo recordings, we tested the hypothesis that neuromodulatory changes in acetylcholine (ACh) levels during non-rapid eye movement (NREM) sleep mediate stabilization of network-wide firing patterns, with temporal order of neurons’ firing dependent on their mean firing rate during wake. In both reduced models and in vivo recordings from mouse hippocampus, we find that the relative order of firing among neurons during NREM sleep reflects their relative firing rates during prior wake. Our modeling results show that this remapping of wake-associated, firing frequency-based representations is based on NREM-associated changes in neuronal excitability mediated by ACh-gated potassium current. We also show that learning-dependent reordering of sequential firing during NREM sleep, together with spike timing-dependent plasticity (STDP), reconfigures neuronal firing rates across the network. This rescaling of firing rates has been reported in multiple brain circuits across periods of sleep. Our model and experimental data both suggest that this effect is amplified in neural circuits following learning. Together our data suggest that sleep may bias neural networks from firing rate-based towards phase-based information encoding to consolidate memories.

          Author summary

          We show that neuromodulatory changes during non-rapid eye movement (NREM) sleep generate stable spike timing relationships between neurons, the ordering of which reflects the neurons’ relative firing rates during wake. Learning-dependent ordering of firing in the hippocampus during NREM, acting in tandem with spike timing-dependent plasticity, reconfigures neuronal firing rates across the hippocampal network. This “rescaling” of neuronal firing rates has recently been reported in multiple brain circuits across periods of sleep. Together, our results suggest that the brain is remapping frequency-biased representations of information formed during wake into timing biased-representations during NREM sleep.

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          Mechanisms of gamma oscillations.

          Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.
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            Sleep and the price of plasticity: from synaptic and cellular homeostasis to memory consolidation and integration.

            Sleep is universal, tightly regulated, and its loss impairs cognition. But why does the brain need to disconnect from the environment for hours every day? The synaptic homeostasis hypothesis (SHY) proposes that sleep is the price the brain pays for plasticity. During a waking episode, learning statistical regularities about the current environment requires strengthening connections throughout the brain. This increases cellular needs for energy and supplies, decreases signal-to-noise ratios, and saturates learning. During sleep, spontaneous activity renormalizes net synaptic strength and restores cellular homeostasis. Activity-dependent down-selection of synapses can also explain the benefits of sleep on memory acquisition, consolidation, and integration. This happens through the offline, comprehensive sampling of statistical regularities incorporated in neuronal circuits over a lifetime. This Perspective considers the rationale and evidence for SHY and points to open issues related to sleep and plasticity. Copyright © 2014 Elsevier Inc. All rights reserved.
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              Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type

              In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb’s rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.
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                Author and article information

                Contributors
                Role: Formal analysisRole: SoftwareRole: Writing – original draft
                Role: Formal analysisRole: SoftwareRole: Writing – review & editing
                Role: Data curationRole: Formal analysis
                Role: Data curation
                Role: Data curationRole: Investigation
                Role: Funding acquisitionRole: InvestigationRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: Project administrationRole: SupervisionRole: 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
                20 September 2021
                September 2021
                : 17
                : 9
                : e1009424
                Affiliations
                [1 ] Biophysics Program, University of Michigan, Ann Arbor, Michigan, United States of America
                [2 ] Applied Physics Program, University of Michigan, Ann Arbor, Michigan, United States of America
                [3 ] Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, Michigan, United States of America
                [4 ] Department of Physics, University of Michigan, Ann Arbor, Michigan, United States of America
                National Research Council, ITALY
                Author notes

                The authors have declared that no competing interests exist.

                [¤]

                Current address: Leiden University Medical Center, Leiden, the Netherlands

                Author information
                https://orcid.org/0000-0002-5659-6558
                https://orcid.org/0000-0002-2455-6973
                https://orcid.org/0000-0003-4916-4201
                https://orcid.org/0000-0001-9978-7461
                https://orcid.org/0000-0002-0807-7526
                https://orcid.org/0000-0002-2921-0509
                https://orcid.org/0000-0002-1722-986X
                Article
                PCOMPBIOL-D-21-00901
                10.1371/journal.pcbi.1009424
                8483332
                34543284
                700188eb-224c-4294-b5b6-15649a1b73db
                © 2021 Skilling 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
                : 15 May 2021
                : 6 September 2021
                Page count
                Figures: 7, Tables: 0, Pages: 27
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000025, National Institute of Mental Health;
                Award ID: DP2MH104119
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000070, National Institute of Biomedical Imaging and Bioengineering;
                Award ID: R01EB018297
                Award Recipient :
                Support for this research was provided by the NIMH: DP2MH104119 (SA) and NIBIB: R01EB018297 (MZ). 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
                Physiology
                Physiological Processes
                Sleep
                Computer and Information Sciences
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Neural Networks
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Memory Consolidation
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Memory Consolidation
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognition
                Memory
                Biology and Life Sciences
                Neuroscience
                Learning and Memory
                Memory
                Biology and Life Sciences
                Computational Biology
                Computational Neuroscience
                Coding Mechanisms
                Biology and Life Sciences
                Neuroscience
                Computational Neuroscience
                Coding Mechanisms
                Biology and Life Sciences
                Anatomy
                Brain
                Hippocampus
                Medicine and Health Sciences
                Anatomy
                Brain
                Hippocampus
                Computer and Information Sciences
                Network Analysis
                Custom metadata
                vor-update-to-uncorrected-proof
                2021-09-30
                The simulation and analysis code are publicly available at https://github.com/eniwbola/Remapping_of_neural_code_during_NREM.git.

                Quantitative & Systems biology
                Quantitative & Systems biology

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