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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
Authors:Quinton M. Skilling  Bolaji Eniwaye  Brittany C. Clawson  James Shaver  Nicolette Ognjanovski  Sara J. Aton  Michal Zochowski
Affiliation: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
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.
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