  We developed a model of cortical computation that implements key features of
cortical circuitry and is capable of describing propagation of neural signals
between cortical locations in response to spatially distributed stimuli. The
model is based on the canonical neural circuit that consists of excitatory and
inhibitory cells interacting through reciprocal connections, with recurrent
feedback. The canonical circuit is used as a node in a distributed network with
nearest neighbor coupling between the nodes. We find that this system is
characterized by intrinsic preference for spatial frequency. The value of
preferred frequency depends on the relative weights of excitatory and
inhibitory connections between cells. This balance of excitation and inhibition
changes as stimulus contrast increases, which is why intrinsic spatial
frequency is predicted to change with contrast in a manner determined by
stimulus temporal frequency. The dynamics of network preference is consistent
with properties of the cortical area MT in alert macaque monkeys.
