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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Learning-Induced Sequence Reactivation During Sharp-Wave Ripples: A Computational Study
verfasst von : Paola Malerba, Katya Tsimring, Maxim Bazhenov
Erschienen in: Advances in the Mathematical Sciences
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Abstract
During sleep, memories formed during the day are consolidated in a dialogue between cortex and hippocampus. The reactivation of specific neural activity patterns—replay—during sleep has been observed in both structures and is hypothesized to represent a neuronal substrate of consolidation. In the hippocampus, replay happens during sharp-wave ripple complexes (SWR), when short bouts of excitatory activity in area CA3 induce high-frequency oscillations in area CA1. In particular, recordings of hippocampal cells which spike at a specific location (“place cells”) show that recently learned trajectories are reactivated during CA1 ripples in the following sleep period. Despite the importance of sleep replay, its underlying neural mechanisms are still poorly understood.
We used a previously developed model of sharp-wave ripples activity, to study the effects of learning-induced synaptic changes on spontaneous sequence reactivation during CA3 sharp waves. In this study, we implemented a paradigm including three epochs: Pre-sleep, learning, and Post-sleep activity. We first tested the effects of learning on the hippocampal network activity through changes in a minimal number of synapses connecting selected pyramidal cells. We then introduced an explicit trajectory-learning task to the learning portion of the paradigm, to obtain behavior-induced synaptic changes. Our analysis revealed that recently learned trajectories were reactivated during sleep more often than other trajectories in the training field. This study predicts that the gain of reactivation rate during sleep following vs sleep preceding learning for a trained sequence of pyramidal cells depends on Pre-sleep activation of the same sequence, and on the amount of trajectory repetitions included in the training phase.