Abstract
Gain modulation is a nonlinear way in which neurons combine information
from two (or more) sources, which may be of sensory, motor, or cognitive
origin. Gain modulation is revealed when one input, the modulatory
one, affects the gain or the sensitivity of the neuron to the other
input, without modifying its selectivity or receptive field properties.
This type of modulatory interaction is important for two reasons.
First, it is an extremely widespread integration mechanism; it is
found in a plethora of cortical areas and in some subcortical structures
as well, and as a consequence it seems to play an important role
in a striking variety of functions, including eye and limb movements,
navigation, spatial perception, attentional processing, and object
recognition. Second, there is a theoretical foundation indicating
that gain-modulated neurons may serve as a basis for a general class
of computations, namely, coordinate transformations and the generation
of invariant responses, which indeed may underlie all the brain functions
just mentioned. This article describes the relationships between
computational models, the physiological properties of a variety of
gain-modulated neurons, and some of the behavioral consequences of
damage to gain-modulated neural representations.
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