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. While 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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