The paper presents a continuous attractor neural network (CANN) model that explains the theta phase shift of hippocampal place cells. The key insights are:
The interplay between the intrinsic mobility of the network bump (due to firing rate adaptation) and the extrinsic mobility (due to location-dependent sensory inputs) leads to an oscillatory tracking state, where the network bump sweeps back and forth around the external input at theta frequency.
The forward and backward sweeps of the network bump account for the theta phase precession and procession observed in individual place cells, respectively.
The adaptation strength controls whether a place cell exhibits only predominant phase precession (unimodal cells) or interleaved phase precession and procession (bimodal cells).
The model also explains other experimental observations, such as the constant cycling of theta sweeps in a T-maze environment, the speed modulation of place cell firing frequency, and the continued phase shift after transient silencing of the hippocampus.
Overall, the model provides a unified mechanistic explanation for the rich dynamics of hippocampal place cell activity during spatial navigation.
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by Chu,T., Ji,Z... 於 www.biorxiv.org 11-14-2022
https://www.biorxiv.org/content/10.1101/2022.11.14.516400v4深入探究