Abstract for Emilio Salinas
Gain modulation is a change in the response amplitude of a neuron that
is independent of its selectivity or receptive field characteristics.
Populations of gain-modulated neurons integrate sensory information
with other kinds of sensory inputs or with proprioceptive, efference-copy,
or attentional signals. Typically this combination is multiplicative,
but the key property is that it is nonlinear. Based on theoretical
results and computer simulations, it will be argued that these distributed,
multi-modal representations are ideally configured to facilitate certain
kinds of computations, most prominently, coordinate transformations.
Two examples will be discussed. One from parietal cortex, where gain
modulation depends on eye position and is crucial for reaching movements,
and another from cortical area V4, where gain modulation depends on attention
and may underlie our capacity to recognize objects independently of eye
position. It is suggested that gain modulation is a prime example
of a general neural design serving a computational purpose.