Primary visual cortex (V1) is absolutely necessary for normal visual processing, but whether V1 encodes upcoming 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 hypothesized that the choice cannot be decoded with classical decoding schemes due to the noise in incorrect trials, but it might be possible to decode with generalized learning. We trained the decoder in the presence of the information on both the stimulus class and the correct behavioral choice. The learned structure of population responses was then utilized to decode trials that differ in the choice alone. We show that with such generalized learning scheme, 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 show that the representation of the choice is primarily carried by bursting neurons in the superficial layer of the cortex. We demonstrated how bursting of single neurons and noise correlations between neurons with similar decoding selectivity helps the accumulation of the choice signal.
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