RT Journal Article
SR Electronic
T1 Neural mechanisms of context-dependent segmentation tested on large-scale recording data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.04.25.441363
DO 10.1101/2021.04.25.441363
A1 Toshitake Asabuki
A1 Tomoki Fukai
YR 2021
UL http://biorxiv.org/content/early/2021/04/26/2021.04.25.441363.abstract
AB The brain performs various cognitive functions by learning the spatiotemporal salient features of the environment. This learning likely requires unsupervised segmentation of hierarchically organized spike sequences, but the underlying neural mechanism is only poorly understood. Here, we show that a recurrent gated network of neurons with dendrites can context-dependently solve difficult segmentation tasks. Dendrites in this model learn to predict somatic responses in a self-supervising manner while recurrent connections learn a context-dependent gating of dendro-somatic current flows to minimize a prediction error. These connections select particular information suitable for the given context from input features redundantly learned by the dendrites. The model selectively learned salient segments in complex synthetic sequences. Furthermore, the model was also effective for detecting multiple cell assemblies repeating in large-scale calcium imaging data of more than 6,500 cortical neurons. Our results suggest that recurrent gating and dendrites are crucial for cortical learning of context-dependent segmentation tasks.Competing Interest StatementThe authors have declared no competing interest.