This introductory neuroscience provides basic understanding of neuronal systems and their respective mathematical models that describes the behavior of the neurons under various conditions. The aim of this course is to encourage Computational biology students to diversify into the area of neuroscience. This course in not about neural networks and machine learning, but about the use of the tools of dynamical systems theory to undertand oscillatory properties of single cell neurons. Nonlinear ODE and PDE models will be constructed, analyzed and simulated using MATLAB to understand different firing patterns of the neuronal systems under normal and pathalogical conditions.
Explain and classify different properties of neurons like spike, threshold, depolarization and electrophysiological properties of neurons.
Develop and analyze computational models of neurons like Hodgkin-Huxley and Integrate and Fire models. Introduce neuronal simulations.
Distinguish and construct various phase portraits of Hodgkin-Huxley model to understand different dynamical properties of neurons through simulations.
Ability to build simple PDE models of diffusion to understand signal propagation in neuronal systems.
Hypothesizing, designing and analyzing new models of neurons for different pathological cases.