time organized maps – learning cortical topography from spatiotemporal stimuli “ learning...

Time Organized Maps – Learning cortical topography from spatiotemporal stimuli

“Learning cortical topography from spatiotemporal stimuli”, J. Wiemer, F. Spengler, F. Joublin, P. Stagge, S. Wacquant, Biological Cybernetics, 2000

“The Time-Organized Map Algorithm: Extending the Self-Organizing Map to Spatiotemporal Signals”, Jan C.Wiemer, Neural Computation, 2003

Presented by: Mojtaba Solgi

Outline

1. The purpose and biological motivation

2. The Model: TOM Algorithm• Wave propagation• Learning

3. Experiments and Results• Gaussian stimuli• Generic artificial stimuli• Semi-natural stimuli

4. Discussion

5. z

Neurobiological experiments, Spengler et al., 1996, 1999

Terminology

Integration

Fusion of different stimuli into one representation

Segregation:

Process of Increasing representational distance of different stimuli

z

2D Network Architecture Activation positional shift

One-dimensional model

Wave propagation

Integration and Segregation

Algorithm

1. Compute neurons activations and the position of the top winner neuron

2. Compute the neural position of the propagated wave from the last time step activation

Algorithm – Cont.

3. Shift the position of the top winner neuron due to interaction with propagated wave

Algorithm – Cont.

4. Again shift the position of the winner neuron this time due to noise

5. Update the winner neurons weights SOM Hebbian

Experiments with Gaussian stimuli & 2D neural layer

1. Simulation of ‘ontogenesis’ (Development)

Experiments with Gaussian stimuli & 2D neural layer

2. Simulation of post-ontogenetic plasticity

One-dimensional model

Experiments with generic artificial stimuli & 1D neural layer

The input

Experiments with semi-natural stimuli & 1D neural layer

Experiments with semi-natural stimuli & 1D neural layer

Discussion

Importance of temporal stimulus for development of cortical topography

Continuous mapping of related stimuli

Inter-Stimulus-Interval-Dependant representations

Hardly scalable

No recognition performance on real-world problems

Tested only on artificial input

Summary

Utilizing temporal information in developing cortical topography

Wave-like spread of cortical activity

Experiments and results show compatibility of the model with neurobiological observations

Biologically inspired and plausible, but no engineering performance

Thank you!

Any thoughts/question?