Closed-loop neuronal computations: focus on vibrissa somatosensation in rat

Abstract

Two classes of neuronal architectures dominate in the ongoing debate on the nature of computing by nervous systems. The first is a predominantly feedforward architecture, in which local interactions among neurons within each processing stage play a less influential role compared with the drive of the input to that stage. The second class is a recurrent network architecture, in which the local interactions among neighboring neurons dominate the dynamics of neuronal activity so that the input acts only to bias or seed the state of the network. The study of sensorimotor networks, however, serves to highlight a third class of architectures, which is neither feedforward nor locally recurrent and where computations depend on large-scale feedback loops. Findings that have emerged from our laboratories and those of our colleagues suggest that the vibrissa sensorimotor system is involved in such closed-loop computations. In particular, single unit responses from vibrissa sensory and motor areas show generic signatures of phase-sensitive detection and control at the level of thalamocortical and corticocortical loops. These loops are likely to be components within a greater closed-loop vibrissa sensorimotor system, which optimizes sensory processing.