脉冲神经网络模型显示 theta 序列支持实时空间记忆提取

Oscillations play a critical role in orchestrating the timing of neural activity during cognitive processes. In the hippocampus, place cells encode spatial locations, and their activity is organized by theta oscillations into sequences that represent past, present, and potential future states. Recently, scrambling theta oscillations in vivo was shown to impair memory retrieval during navigation and decision-making, while leaving place cell activity intact. This surprising result highlights that the exact mechanisms underlying real-time memory encoding and retrieval in the hippocampus remain undefined. Here, we use a spiking neural network model of hippocampal theta sequences to evaluate the consequences of theta frequency scrambling on both representational learning and real-time spatial memory retrieval. We first show that frequency scrambling disrupts theta sequences and phase precession. Specifically, increased frequency scrambling was associated with a gradually degraded relationship between theta phase and spike timing, and ultimately abolished the predictive nature of place fields. When hippocampal spikes are required to solve a T-maze alternation task, the number of successful trials and navigation efficiency decreased with increasing values of frequency scrambling. These results could advance our understanding of how theta modulated neural activity supports memory-guided decision-making in real time and demonstrate why navigation fails when disrupting theta, despite intact spatial representations.

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