Yoshida Kensuke, Toyoizumi Taro
Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan.
Department of Mathematical Informatics, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
PNAS Nexus. 2022 Dec 10;2(1):pgac286. doi: 10.1093/pnasnexus/pgac286. eCollection 2023 Jan.
Slow waves during the non-rapid eye movement (NREM) sleep reflect the alternating up and down states of cortical neurons; global and local slow waves promote memory consolidation and forgetting, respectively. Furthermore, distinct spike-timing-dependent plasticity (STDP) operates in these up and down states. The contribution of different plasticity rules to neural information coding and memory reorganization remains unknown. Here, we show that optimal synaptic plasticity for information maximization in a cortical neuron model provides a unified explanation for these phenomena. The model indicates that the optimal synaptic plasticity is biased toward depression as the baseline firing rate increases. This property explains the distinct STDP observed in the up and down states. Furthermore, it explains how global and local slow waves predominantly potentiate and depress synapses, respectively, if the background firing rate of excitatory neurons declines with the spatial scale of waves as the model predicts. The model provides a unifying account of the role of NREM sleep, bridging neural information coding, synaptic plasticity, and memory reorganization.
非快速眼动(NREM)睡眠期间的慢波反映了皮质神经元的上下交替状态;整体慢波和局部慢波分别促进记忆巩固和遗忘。此外,不同的 spike-timing-dependent 可塑性(STDP)在这些上下状态中起作用。不同可塑性规则对神经信息编码和记忆重组的贡献仍然未知。在这里,我们表明,皮质神经元模型中用于信息最大化的最优突触可塑性为这些现象提供了统一的解释。该模型表明,随着基线放电率的增加,最优突触可塑性偏向于抑制。这一特性解释了在上下状态中观察到的不同 STDP。此外,如果如模型所预测的那样,兴奋性神经元的背景放电率随着波的空间尺度而下降,那么它解释了整体慢波和局部慢波如何分别主要增强和抑制突触。该模型对 NREM 睡眠的作用提供了统一的解释,将神经信息编码、突触可塑性和记忆重组联系起来。