Modeling of neuronal networks through decomposition

The authors present an approach to modeling the functional properties of a neuronal network as the composite of input/output functions characterizing each physiologically meaningful elemental unit in the network. These relationships capture the dynamics of the elemental units in a common analytical framework, independent of the hierarchic level at which they are measured. This framework allows characterizing functions for unobservable portions of the network to be computed and recombined according to hypothesized topologies of the network under study. An example that applies this approach to the hippocampal formation, a neuronal network important in a variety of cognitive functions, is presented.<>