Section A
Overview of biological neurons: Structure of biological neurons relevant to ANNs.
Fundamental concepts of Artificial Neural Networks: Models of ANNs; Feedforward & feedback
networks; learning rules; Hebbian learning rule, perception learning rule, delta learning rule, Widrow-Hoff
learning rule, correction learning rule, Winner –lake all elarning rule, etc.
Section B
Single layer Perception Classifier: Classification model, Features & Decision regions; training &
classification using discrete perceptron, algorithm, single layer continuous perceptron networks for
linearlyseperable classifications.
Multi-layer Feed forward Networks: linearly non-seperable pattern classification, Delta learning rule for
multi-perceptron layer, Generalized delta learning rule, Error back-propagation training, learning factors,
Examples.
Section C
Single layer feed back Networks: Basic Concepts, Hopfield networks, Training & Examples.
Associative memories: Linear Association, Basic Concepts of recurrent Auto associative memory:
rentrieval algorithm, storage algorithm; By directional associative memory, Architecture,
Association encoding & decoding, Stability.
Section D
Self organizing networks: UN supervised learning of clusters, winner-take-all learning, recall mode,
Initialisation of weights, seperability limitations