FORENOON

Introduction: Srinivasa Chakravarthy

Theory session 1: Nurani Rajagopal Rohan

This session motivates the need for development of general trainable oscillator networks. In response to that motivation, it presents results from networks of Hopf oscillators that can: 

1. Store and generate arbitrary vector signals

2. Learn mapping between arbitrary vector signals.

3. The basic results are presented and the learning algorithms are described.

Practical session 1: Nurani Rajagopal Rohan

This session demonstrates working of the following models.

1.   Single Hopf oscillator.

2.  A pair of coupled Hopf oscillators with complex coupling.

3.  A pair of coupled Hopf oscillators with power coupling.

4.  A network of Hopf oscillators for signal generation.

5.  A network of Hopf oscillators for signal transformation.

Theory Session 2: Sayan Ghosh

Modeling high-dimensional EEG using the neural oscillator network.

 Practical Session 2: Sayan Ghosh 

A demonstration of how to model high-dimensional EEG using the neural oscillator network.

Theory Session 3: Anirban

Modeling fMRI data using the neural oscillator network.

 Practical Session 3: Anirban

A demonstration of how to model fMRI data using the neural oscillator network.

Practical Session 4: Vigneswaran

Demonstration of Deep Oscillator Neural Network