Haim Sompolinsky, Ph.D.
The Racah Institute of Physics
The Hebrew University
April 28, 2003
Excitation-Inhibition Balance: The Search for Functional Principles of Cortical Networks
Neural circuits show a great deal of variability in their responses, even when identical stimuli are presented. The source of this variability has been an ongoing question in studies of neural dynamics. The large magnitude of the variability is especially puzzling in view of the fact that individual neurons integrate large numbers of inputs, and therefore would be expected to average out noise. However, model neural networks that are constructed with (1) large numbers of neurons, (2) sparse connections between neurons, and (3) an approximate balance between excitatory and inhibitory input can reproduce the high levels of variability seen in real neural circuits.
We have constructed such models and explored their properties in great detail using a combination of analytic techniques and computer simulation. In previous models, noise was always injected into models from an unknown external source. It did not arise naturally from the model itself as it does in this work. The general level of variability, the statistics of individual response, and the correlations between responses seen in the model networks match quite well with those measured in vivo. Furthermore, we have applied these ideas to models of primary visual cortex and accounted for a number of features of visual responses seen in recordings from anesthetized and awake animals. Thus, we feel we have a basic understanding of the sources and the consequences of high levels of variability in neural circuits.