Modeling a H-H Neuron based Spiking Neural Network incorporating Multiple Pre-synaptic Inputs

Spiking Neural Networks (SNN) is the latest generation of Artificial Neural Networks that draws strength from the capability to closely mimic its biological counterpart. The choice of the spiking neuron that constitutes the core entity of a SNN determines its effectiveness for a particular application. Certain situations such as "neuron replacement therapy" can demand a precise realization of the biological neural net by the bio-mimetic prosthesis. Such specific prerequisites can only be catered using a network comprising of detailed neuron models that encompasses synaptic interactions between multiple neurons. Hence, in the proposed work, a simple spiking neural network is modeled using a "conjoint" neuron model that amalgamates the strengths and versatilities of the H-H model, Integrate-and-Fire model and Synapse model and thus presents a unique representation of the membrane behaviour due to multiple temporally varying and spatially distributed synaptic inputs.