Indranil Ghosh
Indra is a postdoctoral fellow in applied mathematics at Massey University, New Zealand, working on all things "dynamical systems". He takes a computational approach to tackle complex problems, and his current research is focused on understanding collective behaviour exhibited by coupled neurons. He is an avid Python user and has been a speaker at multiple Python-related conferences before. More information can be found in his website: https://indrag49.github.io/.
Session
The complex nervous system provides a repertoire of evolutionary properties like neuron spiking, bursting, and chaos that are yet to be fully understood. One approach is to tackle these time-dependent properties using the technique of "dynamical systems”, such as ordinary differential equations. Since the popular work by Hodgkin and Huxley, many dynamical systems models of neurons have been proposed, of which FitzHugh–Nagumo and Morris–Lecar models draw special attention. The nervous system is made of a network of neurons, possessing a complex structural and functional topology. This topology is a function of different parameters, among which the coupling strength plays a major role. Our focus would be to systematically study the effect of various coupling strategies on the firing patterns exhibited by a collection of neurons. In this workshop, my goal is to popularize a reduced-order model of neuron dynamics known as the “denatured Morris–Lecar” system and to teach how Python can be efficiently used to perform research on time series analysis of coupled neurons.