<p>The elementary processing units in brain are neurons which are connected to each other in many shapes and sizes. A typical neuron can be divided into functionally three distinct parts called Dendrites, Soma and Axon. Dendrites play the role of input device that collect signals from other neurons and transmits them to soma. Soma performs a Non-linear operation, i.e. if input exceeds a certain threshold, an output signal is generated. This output signal is taken over by an output device, the Axon, which delivers the signal to other neurons. This is the basic function of a biological neuron. A biological neuron model which is also known as Spiking Neuron Model is a mathematical description of properties of neuron that is to be designed accurately to describe and predict the biological processes. So there comes the concept of modelling and analysis of neurons. Modelling and analysis of neurons was performed by different researchers on First, Second and Third generation of neurons. The Third generation of neurons are also called as spiking neurons. The focus of this work is to implement different types of spiking neuron models developed by Izhikevich which is a mathematical model and Hodgkin and Huxley which is a biological model. Comparison between these two models in terms of Design implementation has been done. These both model simulations are done in MATLAB and they are modelled using digital logic circuits in Verilog Hardware Description Language (HDL) and simulated in ModelSIM RTL simulator. These models are then implemented in Xilinx FPGA and checked for the functionality. © 2016 IEEE.</p>
cited By 0; Conference of 2016 International Conference on Advanced Communication Control and Computing Technologies, ICACCCT 2016 ; Conference Date: 25 May 2016 Through 27 May 2016; Conference Code:126081
J. Kumar, Kumar, J., Murali, S., and Dr. Ramesh Bhakthavatchalu, “Design and implementation of Izhikevich, Hodgkin and Huxley spiking neuron models and their comparison”, in Proceedings of 2016 International Conference on Advanced Communication Control and Computing Technologies, ICACCCT 2016, 2016, pp. 111-116.