Nonlinear high-order model for dynamic synapse with multiple vesicle pools

Abstract

A computational framework for studying nonlinear dynamic synapses is proposed in this chapter. The framework is based on biological observation and electrophysiological measurement of synaptic function. The "pool framework" results in a model composed of four vesicle pools that are serially connected in a loop. The vesicle release event is affected by facilitation and depression in a multiple-order fashion between the presynapse and the postsynapse. The proposed high-order dynamic synapse (HODS) model, using fewer parameters, predicts the experimental data recorded from Schaffer collateral - CA1 synapses under different experimental conditions better than the basic additive dynamic synapse (DS) model and the basic multiplicative facilitation-depression (FD) model. Numerical study shows that the proposed model is stable and can efficiently explore the dynamic filtering property of the synapse. The proposed model captures biological reality with regard to neurotransmitter communication between the pre- and postsynapse, while neurotransmitter communication between neurons encodes information of cognition and consciousness throughout cortices. It is expected that the present model can be employed as basic computational units to explore neural learning functions in a dynamic neural network framework. © 2007 Springer-Verlag Berlin Heidelberg.