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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