Neural Networks: Neural Systems III
Abstract
Advances in human brain imaging methods have provided a remarkable opportunity for studying the neural basis of human brain function. Usually the goal of these studies is the localization of specific task components to defined brain areas. More recent efforts have attempted to identify functional neural networks in the central nervous system. Still, with either approach, there exists the question of how regional cerebral blood flow (rCBF) data reflect underlying neuronal events such as those measured by electrophysiological recording (e.g., cell firing), since imaging data are an indirect measure of neuronal activity. It is generally thought that rCBF mainly reflects synaptic activity rather than neuronal firing per se. Synaptic activity can lead to an increase in both rCBF and brain oxidative metabolism, whether that activity results in inhibition or excitation. Therefore, both excitatory and inhibitory synaptic activity may increase rCBF even if the inhibition causes a decrease in overall local or distant neuronal firing rates. We have used computational modeling to examine factors that potentially play a role in the relationship between neuronal events and the signals measured in human imaging experiments. The goal of computational modeling is to develop a biologically plausible mathematical model that can simulate