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The computational foundations of dynamic coding in working memory
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DOI：10.1016/j.tics.2024.02.011.png)
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摘要
En 中文
Working memory (WM) is a fundamental aspect of cognition. WM maintenance is classically thought to rely on stable patterns of neural activities. However, recent evidence shows that neural population activities during WM maintenance undergo dynamic variations before settling into a stable pattern. Although this has been difficult to explain theoretically, neural network models optimized for WM typically also exhibit such dynamics. Here, we examine stable versus dynamic coding in neural data, classical models, and task-optimized networks. We review principled mathematical reasons for why classical models do not, while task-optimized models naturally do exhibit dynamic coding. We suggest an update to our understanding of WM maintenance, in which dynamic coding is a fundamental computational feature rather than an epiphenomenon.
Keyword:
LOW-DIMENSIONAL DYNAMICS
PREFRONTAL CORTEX
NEURAL ACTIVITY
NETWORK
TIME
MODELS
REPRESENTATIONS
INFORMATION
MECHANISMS
GENERATION
期刊
IF:
17.2
论文数:
3.6K
被引数:
3.5W
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引用论文
Synaptic reverberation underlying mnemonic persistent activity助眠持续活动的突触混响.png)
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