Normalization has been proposed as a canonical computation that accounts for a variety of nonlinear neuronal response properties associated with sensory processing and higher cognitive functions. A key premise of normalization is that the excitability of a neuron is inversely proportional to the overall activity level of the network. We tested this by optogenetically activating excitatory neurons in alert macaque primary visual cortex and measuring changes in neuronal activity as a function of stimulation intensity, with or without variable-contrast visual stimulation. Optogenetic depolarization of excitatory neurons either facilitated or suppressed baseline activity, consistent with indirect recruitment of inhibitory networks. As predicted by the normalization model, neurons exhibited sub-additive responses to optogenetic and visual stimulation, which depended lawfully on stimulation intensity and luminance contrast. We conclude that the normalization computation persists even under the artificial conditions of optogenetic stimulation, underscoring the canonical nature of this form of neural computation. The Normalization Model can account for a wide variety of neural computations, ranging from contrast gain control to attentional selection. Here, Nassi et al. find strong support for the model by combining optogenetic and visual stimulation in primate visual cortex.
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