Department of Physiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, 545-8585, Japan
Neural Comput. 2024 Mar 21;36(4):501-548. doi: 10.1162/neco_a_01641.
The hippocampus plays a critical role in the compression and retrieval of sequential information. During wakefulness, it achieves this through theta phase precession and theta sequences. Subsequently, during periods of sleep or rest, the compressed information reactivates through sharp-wave ripple events, manifesting as memory replay. However, how these sequential neuronal activities are generated and how they store information about the external environment remain unknown. We developed a hippocampal cornu ammonis 3 (CA3) computational model based on anatomical and electrophysiological evidence from the biological CA3 circuit to address these questions. The model comprises theta rhythm inhibition, place input, and CA3-CA3 plastic recurrent connection. The model can compress the sequence of the external inputs, reproduce theta phase precession and replay, learn additional sequences, and reorganize previously learned sequences. A gradual increase in synaptic inputs, controlled by interactions between theta-paced inhibition and place inputs, explained the mechanism of sequence acquisition. This model highlights the crucial role of plasticity in the CA3 recurrent connection and theta oscillational dynamics and hypothesizes how the CA3 circuit acquires, compresses, and replays sequential information.
海马体在序列信息的压缩和检索中起着关键作用。在清醒状态下，它通过θ相位超前和θ序列来实现这一点。随后，在睡眠或休息期间，通过尖峰涟漪事件使压缩信息重新激活，表现为记忆回放。然而，这些序列神经元活动是如何产生的，以及它们如何存储关于外部环境的信息，仍然未知。我们基于生物 CA3 回路的解剖和电生理证据，开发了一个海马体 CA3 计算模型，以解决这些问题。该模型包括θ节律抑制、位置输入和 CA3-CA3 可塑性递归连接。该模型可以压缩外部输入的序列，再现θ相位超前和回放，学习额外的序列，并重新组织以前学习的序列。通过θ节拍抑制和位置输入之间的相互作用控制的突触输入的逐渐增加，解释了序列获取的机制。该模型强调了 CA3 递归连接和θ振荡动力学中的可塑性的关键作用，并假设 CA3 电路如何获取、压缩和回放序列信息。
