期刊
JOURNAL OF NEUROSCIENCE
卷 34, 期 48, 页码 16046-16057出版社
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.2851-14.2014
关键词
computational modeling; decision-making; divisive normalization; dynamical system; reward
资金
- National Eye Institute [R01-EY010536]
- NATIONAL EYE INSTITUTE [R01EY010536] Funding Source: NIH RePORTER
Normalization is a widespread neural computation, mediating divisive gain control in sensory processing and implementing a context-dependent value code in decision-related frontal and parietal cortices. Although decision-making is a dynamic process with complex temporal characteristics, most models of normalization are time-independent and little is known about the dynamic interaction of normalization and choice. Here, we show that a simple differential equation model of normalization explains the characteristic phasic-sustained pattern of cortical decision activity and predicts specific normalization dynamics: value coding during initial transients, time-varying value modulation, and delayed onset of contextual information. Empirically, we observe these predicted dynamics in saccade-related neurons in monkey lateral intraparietal cortex. Furthermore, such models naturally incorporate a time-weighted average of past activity, implementing an intrinsic reference-dependence in value coding. These results suggest that a single network mechanism can explain both transient and sustained decision activity, emphasizing the importance of a dynamic view of normalization in neural coding.
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