1. To Understand, Survey and Implement Neurodynamic Models By Farhan Tauheed Asif Tasleem

2. Project Progress ► Literature Review  Temporal Networks ► Specific Problem for Implementation  Implications ► Architectural Plan for Implementation  Formal definition

3. Motivation ► Machine Perception ► Biological aspects of Traditional Neural Network Models  Summation neuron  Non Linear Activation function ► Non biological aspects  Static  Continuous Input  Back propagation learning algorithm

4. Temporal Neural Networks ► Biologically Inspired  Continuous data feed is operated on  Dynamic Model  Long term Memory  Short term Memory ► Tapped delay line ► Distributed Time Lagged Feed forward NNs  Different Back Propagation algorithm

5. Literature Review ► Universal Myopic Mapping theorem  Any uniform fading memory mapped behind a static network can simulate just as well ► Fontine and Shastri 1993. have demostrated that certain tasks not having an explicit temporal aspect can also be processed advantageously by Temporal Networks ► Thompson(1996) “Completeness of BSS”

6. Related Problems ► Time Series Data Prediction ► Blind Signal Separation ► Cocktail Party Problem ► Attention Based Search Optimization ► Visual Pattern Recognition

7. Blind Signal Separation Implication ► Speech Recognition (phoneme recognition) ► Multimedia Compression ► MM database sound based retrieval ► Noise Removal ► Audio Analysis and Visualization ► Sonar and Radar ► Cache Hit Algorithms

8. Architectural Plan ► Formal Problem Description ► x[i]s input. each x[i] is a mixture of a number of constituent signals u[j]s we need to separated out/ deconvolute the u[j]s from x[i]s. ► Frequency Domain ► Multilayered Network  Hebbian Learning rule

9. To work on ► Neurodynamics theorems  Stability issues ► Oscillatory / Pulsating Neural Networks THE END