在 Theta 振荡和重放中压缩序列信息的 CA3 电路模型。

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Satoshi Kuroki;Kenji Mizuseki
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引用次数: 0

摘要

海马体在序列信息的压缩和检索中发挥着至关重要的作用。在清醒状态下,海马体通过θ相位前冲和θ序列实现这一功能。随后,在睡眠或休息期间,被压缩的信息通过锐波波纹事件重新激活,表现为记忆重放。然而,这些连续的神经元活动是如何产生的,它们又是如何存储外部环境信息的,这些仍然是未知数。为了解决这些问题,我们基于生物 CA3 电路的解剖学和电生理学证据,建立了一个海马角弓 3(CA3)计算模型。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,重现θ相位前冲和重放,学习额外的序列,并重组以前学习过的序列。在θ步抑制和位置输入的相互作用控制下,突触输入的逐渐增加解释了序列习得的机制。该模型强调了可塑性在CA3递归连接和θ振荡动态中的关键作用,并假设了CA3回路是如何获取、压缩和重放序列信息的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CA3 Circuit Model Compressing Sequential Information in Theta Oscillation and Replay
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.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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