Topic: correlated activity

Layer 5 pyramidal neurons (L5 PNs) are a major cortical output. Their connectivity approximates a recurrent neural network (RNN), with the majority of inputs coming from other L5 PNs. These neurons may underlie anesthesia-induced unconsciousness, by restricting information output from cortex. Therefore, understanding the formation of the L5 PN circuitry, from the time at which L5 PNs first enter cortex, would provide insight into how a biological RNN can be constructed. However, this is technically challenging because, in mice, L5 PNs first enter cortex at embryonic day (E) 13.5, five days bef

Correlated activity between neurons can cause variability in behavior across trials, as trial-by-trial neuronal noise will propagate through the motor system. The extent to which correlated activity affects behavior depends on the properties of its translation into movement. A major hurdle in studying the effect of neural correlations on behavior is the need to assume a model of the relationship between neural activity and behavior. We developed a novel method that estimates the contribution of correlations to behavior, with minimal assumptions. We applied this method to the frontal eye field

Advancements in large-scale neural recordings are now enabling opportunities to probe cognitive computations across simultaneously recorded brain regions, necessitating modeling tools for deciphering multi-region interactions. In an emerging class of dynamical models, brain-wide neural activity is reconstructed by region-specific modules that interact through communication channels and may receive additional information as inputs. These models aim to recover the effective connectome, which includes the inter-region connection diagram and the messages communicated between regions. However, with