| Popis: |
The complete characterization of the activity of a neural population is a challenging task that is complicated by the number of potential interactions that grows exponentially as the size of the population increases. One influential view that has emerged several decades ago is that simple pairwise interactions between neurons can account for the observed firing patterns of large networks. However, despite its prevalence, this view originates from computational and electrophysiological studies in the retina and the primary visual cortex (V1) of anesthetized animals. Therefore, whether or not pairwise interactions predict the observed distribution of firing patterns across multiple brain areas in behaving animals remains unknown. Here we performed multi-electrode recordings from 3 cortical areas to report that second-order neuronal interactions can explain a high fraction of the entropy of the population response in early and mid-level visual cortex (areas V1 and V4) while providing a good approximation of the probability of spiking for groups of neurons. Surprisingly, despite the fact the firing pattern of neurons is controlled by brain state, the model based on pairwise interactions captures more than 90% of the structure in the detailed patterns of spiking observed during wakefulness and sleep. In contrast, regardless of brain state, pairwise interactions fail to explain experimentally observed entropy in neural populations from executive brain areas, such as the dorsolateral prefrontal cortex (dlPFC). These results indicate that higher-order interactions explain the population dynamics in downstream, executive areas, whereas simple pairwise interactions account for the dynamic behavior of neuronal networks in sensory cortical areas. |
