time organized maps – learning cortical topography from spatiotemporal stimuli “ learning...
Post on 20-Dec-2015
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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?