Compartmental modelling with artificial neural networks

摘要

In this paper, we introduce an abbreviated compartmental modelling scheme which may be of interest to those in neuron- based adaptive systems because of the additional scope it provides for studying biologically-inspired learning mechanisms. The scheme, although not as flexible and precise as the general compartmental approach, allows one to design Hodgkin-Huxley style cells, and passive dendritic trees with an arbitrary number of synaptic connections. The trade-offs made for computational performance, may make the modelling scheme more appropriate for practical applications. The modelling scheme is based upon artificial neural networks, which we have used to represent cylindrical compartments (both passive and active) of different lengths, two types of voltage-dependent channels, and basic chemical synapses with variable time constants.