期刊
NEURAL COMPUTATION
卷 32, 期 6, 页码 1033-1068出版社
MIT PRESS
DOI: 10.1162/neco_a_01280
关键词
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资金
- Simons Foundation MMLS investigator program
- University of Chicago Research Computing Center
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which the emergent bump of neural activity in such networks can bemanipulated by space and time-dependent external sensory or motor signals are not understood. Here, we find fundamental limits on how rapidly internal representations encoded along continuous attractors can be updated by an external signal. We apply these results to place cell networks to derive a velocity-dependent nonequilibrium memory capacity in neural networks.
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