To Understand, Survey and Implement Neurodynamic Models

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
• xis input.
• each xi is a mixture of a number of
  constituent signals ujs
• we need to separated out/ deconvolute the ujs
  from xis.
• Frequency Domain
• Multilayered Network
• Hebbian Learning rule

9
To work on

• Neurodynamics theorems
• Stability issues
• Oscillatory / Pulsating Neural Networks
• THE END