JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives

A survey of common modeling techniques, methods for extending traditional

techniques

Course Outcomes

Students would be able to:

• 1. Acquire an introductory understanding of the basic principles of computational neuroscience (including extensions to parallel distributed processing (PDP), connectionist, and artificial neural network models).
• 2. Relate the formal properties of such models to known biological mechanisms as well as to behavioral phenomena, and they will possess intellectual tools for modifying such models in the light of new psychological and neuroscientific findings.

UNIT - I

Neural Encoding I: Introduction, Spike Trains and Firing Rates, What Makes a Neuron Fire?, Spike- Train Statistics, The Neural Code Neural Encoding II: Introduction. Estimating Firing Rates, Introduction to the Early Visual System, Reverse-Correlation Methods: Simple Cells, Static Nonlinearities: Complex Cells, Receptive Fields in the Retina and LGN, Constructing V1 Receptive Fields

UNIT - II

Neural Decoding: Encoding and Decoding, Discrimination, Population Decoding, Spike-Train Decoding Information Theory: Entropy and Mutual Information, Information and Entropy Maximization, Entropy and Information for Spike Trains

UNIT - III

Model Neurons I: Introduction, Electrical Properties of Neurons, Single-Compartment Models, Integrate-and-Fire Models, Voltage-Dependent Conductances, The Hodgkin-Huxley Model, Modeling Channels, Synaptic Conductances, Synapses on Integrate-and-Fire Neurons Model Neurons II: Levels of Neuron Modeling, Conductance-Based Models, The Cable Equation, Multi-compartment Models

UNIT - IV

Network Models: Introduction, Firing-Rate Models, Feedforward Networks, Recurrent Networks, Excitatory-Inhibitory Networks, Stochastic Networks Plasticity and Learning: Introduction, Synaptic Plasticity Rules, Unsupervised Learning, Supervised Learning

UNIT - V

Classical Conditioning and Reinforcement Learning: Introduction, Classical, Static Action Choice, Sequential Action Choice Representational Learning: Introduction, Density Estimation, Causal Models for Density Estimation

TEXT BOOKS:

• 1. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Peter Dayan, L.F Abott, MIT Press.
• 2. Computational Neuroscience-A Comprehensive Approach, Chapman & Hall/CRC.