Summary: 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.