Choice Can Be Predicted from Populations of Bursting Neurons in Superficial Layers of Monkey V1

Abstract

Primary visual cortex (V1) is absolutely necessary for normal visual processing, but whether V1 encodes upcom-ing behavioral decisions based on visual information is an unresolved issue, with conflicting evidence. Further,no study so far has been able to predict choice from time-resolved spiking activity in V1. Here, we hypothesizedthat the choice cannot be decoded with classical decoding schemes due to the noise in incorrect trials, but itmight be possible to decode with generalized learning. We trained the decoder in the presence of the informationon both the stimulus class and the correct behavioral choice. The learned structure of population responseswas then utilized to decode trials that differ in the choice alone. We show that with such generalized learningscheme, the choice can be successfully predicted from spiking activity of neural ensembles in V1 in single trials,relying on the partial overlap in the representation between the stimuli and the choice. In addition, we showthat the representation of the choice is primarily carried by bursting neurons in the superficial layer of thecortex. We demonstrated how bursting of single neurons and noise correlations between neurons with similardecoding selectivity helps the accumulation of the choice signal.