Since we started to interact with the outside world, we have learned to distinguish whether the movement is coming from our own actions or from the movement of external objects. To distinguish between these experiences, it is necessary to factor out the sensory consequences of our actions from incoming sensory information. The main theoretical framework accounting for this computation is predictive coding which proposes to understand top-down inputs from higher visual areas as predictions of bottom-up sensory inputs. However, the structural rules underlying these functional properties of cortical circuits are poorly understood. For that matter, our research aims at integrating insights into the architecture and function of laminar local cortical microcircuits in the neocortex. Particularly, we unveil some of the predictive coding mechanisms using a computational model for a microcircuit of area V1 in mice built on a huge body of experimental work published by the Allen Institute.
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