DYNAMICS OF COMPARTMENTAL MODEL NEURONS

Recent developments in the dynamics of compartmental model neurons are described. In particular, an explicit analytical expression for the response function of a neuron with arbitrary dendritic tree structure is presented, which provides a general framework for exploring the effects of complex geometries on spatiotemporal pattern processing in neurons. Some examples of how compartmental structure can enhance a neuron's sensitivity to temporal features of an input are given. Shunting contributions are included at a perturbative level, and are shown to have an important influence on the time constants of a neuron's response. The effects of synaptic background activity are also discussed. A solution to the dynamical equations for a compartmental model with somatic potential reset is presented thal takes proper account of the electrical coupling between soma and dendrites. Finally, some applications of this work to artificial neural networks are indicated, including an extension of the standard error back propagation algorithm to the case of compartmental neurons with known response function.