  We analyze the effects of noise correlations in the input to, or among, BCM
neurons using the Wigner semicircular law to construct random,
positive-definite symmetric correlation matrices and compute their eigenvalue
distributions. In the finite dimensional case, we compare our analytic results
with numerical simulations and show the effects of correlations on the
lifetimes of synaptic strengths in various visual environments. These
correlations can be due either to correlations in the noise from the input LGN
neurons, or correlations in the variability of lateral connections in a network
of neurons. In particular, we find that for fixed dimensionality, a large noise
variance can give rise to long lifetimes of synaptic strengths. This may be of
physiological significance.

Biological Physics;Neurons and Cognition