How Noise Contributes to Contrast Invariance of Orientation Tuning in Cat Visual Cortex
The Journal of Neuroscience. 2002-06-15; 22(12): 5118-5128
DOI: 10.1523/jneurosci.22-12-05118.2002
1. J Neurosci. 2002 Jun 15;22(12):5118-28. doi:
10.1523/JNEUROSCI.22-12-05118.2002.
How noise contributes to contrast invariance of orientation tuning in cat visual
cortex.
Hansel D(1), van Vreeswijk C.
Author information:
(1)Laboratoire de Neurophysique et Physiologie du Système Moteur (EP 1848 Centre
National de la Recherche Scientifique), Université René Descartes, 75270 Paris
cedex 06, France.
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.
DOI: 10.1523/JNEUROSCI.22-12-05118.2002
PMCID: PMC6757721
PMID: 12077207 [Indexed for MEDLINE]