Neural correlations during a cognitive task are central to study brain information processing and computation. However, they have been poorly analyzed due to the difficulty of recording simultaneous single neurons during task performance. In the present work, we quantified neural directional correlations using spike trains that were simultaneously recorded in sensory, premotor, and motor cortical areas of two monkeys during a somatosensory discrimination task. Upon modeling spike trains as binary time series, we used a nonparametric Bayesian method to estimate pairwise directional correlations between many pairs of neurons throughout different stages of the task, namely, perception, working memory, decision making, and motor report. We find that solving the task involves feedforward and feedback correlation paths linking sensory and motor areas during certain task intervals. Specifically, information is communicated by task-driven neural correlations that are significantly delayed across secondary somatosensory cortex, premotor, and motor areas when decision making takes place. Crucially, when sensory comparison is no longer requested for task performance, a major proportion of directional correlations consistently vanish across all cortical areas.
Keywords: decision making; information theory; large-scale cortical networks; spike-train analysis; vibrotactile discrimination.