How noise contributes to contrast invariance of orientation tuning in cat visual cortex.
The width of the orientation tuning curves of the spike response of neurons in V1 is invariant to contrast. This property constrains the possible mechanisms underlying orientation selectivity. It has been suggested that noise circumvents the iceberg effect that would prevent contrast invariance in the purely feedforward mechanism. Here we investigate systematically how noise contributes to the contrast invariance of orientation tuning curves in V1. We study three models of increasing complexity: a simple threshold-linear firing rate model, a leaky integrate-and-fire model, and a conductance-based model. We show that the noise transmutes the threshold nonlinearity of the input-output relationships into an approximate power law without a threshold within some firing rate range. This implies that, under certain conditions which are derived here, the tuning of the neuron output is approximately contrast invariant. In particular we show that this mechanism for contrast invariance requires that the neuron firing rate must not be too large and that increasing or lowering the contrast too much destroys this invariance. We also show that if this mechanism operates in V1, the spike response, R, and average voltage response V of the neurons in V1 should vary with the contrast, C, according to R(C)gamma proportional to V(C)gamma. The exponent gamma can be estimated from the amount by which the spike tuning curve is sharpened with respect to the voltage tuning curves of the neurons. This prediction does not depend on the specifics of the model and can be tested experimentally.
Hansel
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D
Laboratoire de Neurophysique et Physiologie du Système Moteur (EP 1848 Centre National de la Recherche Scientifique), Université René Descartes, 75270 Paris cedex 06, France. david.hansel@biomedicale.univ-paris5.fr
van Vreeswijk
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C
D000818:Animals; D002415:Cats; D004553:Electric Conductivity; D007700:Kinetics; D008564:Membrane Potentials; D008959:Models, Neurological; D008962:Models, Theoretical; D009474:Neurons / Q000502:physiology; D009435:Synaptic Transmission; D014793:Visual Cortex / Q000502:physiology*