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Course Outline
- Introduction to neuroscience: nervous system, sympathetic, parasympathetic and motor nervous system and their functions, brain and its functions. Neurons and glia, structure of a neuronal cell, types of glia, blood brain barriers.
- Signaling in the brain: electrical excitability of neurons, resting membrane potential, action potential, intra neuronal singling, inter neuronal singling. Synaptic events, chemical messengers, synaptic transmission.
- Receptors: Ionotropic and metabotropic receptors, signal transduction pathways, G-proteins, protein phosphorylation. Signaling to the nucleus, regulation of gene expression.
- Neurotransmitters: Excitatory and inhibitory amino acid neurotransmitters and functions in the brain, role of excitatory neurotransmitter in learning and memory. Diseases associated with the malfunctioning of these neurotransmitters.
- Catecholamines: functions in the brain, Diseases associated with the malfunctioning.
- Artificial Neural Network: Model of single neuron, neural network architectures. Feed forward neural networks. Multilayer perception, back propagation algorithm, radial basis function networks. Unsupervised learning. Hopfield network, self organizing map, other unsupervised networks. Reinforcement learning.
Lab Outline
- Study of brain parts and its function.
- Mcculloch pitts neural network architecture and its implementation.
- Mcculloch pitts neural network in logical circuits designing (AND, NAND, OR, NOR, NOT).
- Designing and plotting of transfer functions using MATLAB.
- Perception learning (Supervised training).
- Perceptron training using neural network toolbox.
- Checking necessary conditions of Hebb‟s Rule (Orthonormality).
- Hebb‟s Rule.
- Pseudo Inverse Rule.
An Introduction to Neural Networks by 
