Temporal prediction captures key differences between spiking excitatory and inhibitory V1 neurons

Abstract

Neurons in primary visual cortex (V1) respond to natural scenes with a sparse and irregular spike code that is carefully balanced by an interplay between excitatory and inhibitory neurons. These neuron classes differ in their spike statistics, tuning preferences, connectivity statistics and temporal dynamics. To date, no single computational principle has been able to account for these properties. We developed a recurrently connected spiking network of excitatory and inhibitory units trained for efficient temporal prediction of natural movie clips. We found that the model exhibited simple and complex cell-like tuning, V1-like spike statistics, and, notably, also captured key differences between excitatory and inhibitory V1 neurons. This suggests that these properties collectively serve to facilitate efficient prediction of the sensory future.