# EC60007: Computational Neuroscience

**Title:** Computational Neuroscience

**Teacher:** Sharba Bandyopadhyay, E&ECE

**Credits:** 3 (3-0-0)

**Objectives:**
The objectives of the course is to expose engineers and scientists to
the field of theoretical neuroscience that involves how actual biological neurons
perform computations in the mammalian brain. Current understanding of the
biophysics of computation is completely different from the field of artificial
neural networks. The course covers the very basics of the biology involved in
neuronal processing up to understanding how neurons encode sensory
information and how such information is decoded in the brain. Further the
course covers phenomena of learning and plasticity of neuronal circuits. Overall
the course will equip students to provide different ways of solving problems
related to learning using biologically plausible solutions and also allow
interested candidates to take up research in neuroscience, a highly
interdisciplinary field and contribute in areas of brain machine interface, neural
prosthetics, biological sensors and many others.

**Text Book**
Theoretical Neuroscience - Computational and Mathematical Modeling of Neural
Systems by Peter Dayan and L.F. Abbott

**Reference Books**
Biophysics of Computation by Christof Koch
Ion Channels of Excitable Membranes by Hille
Methods in Neuronal Modeling by Segev
Principles of Neural Science by Kandel and Schwartz
Neuronal Dynamics by Gerstner

Intended as an elective course for: 4th year BTech, 4th & 5th year BTech/MTech BSc/MSc Dual degree, 1st year MTech/MSc students from the Departments of E&ECE, CSE, EE, Physics, Maths, BioTech, SMST etc.

**Prerequisites:** None

**Section 1:** Single Neuron Modeling
Ion flux in membranes, Nernst Planck Equation, Ion-Channels, Excitable
membranes, Spiking, Hodgkin Huxley models, Integrate and Fire Neurons

**Section 2:** Neural Encoding and Decoding
Spike train statistics, Receptive fields, Linear and Nonlinear models of Receptive
fields, Applications of Information Theory in neural coding and decoding

**Section 3:** Plasticity: Adaptation and Learning
Synapses: structure and function, plasticity, Spike Timing Dependent Plasticity
(STDP), Learning rules, Supervised and Unsupervised Learning, Classical
conditioning, Reinforcement Learning