From brain activities to mathematical models The TempUnit model, a study case for GPU computing in scientific computation.

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Presentation transcript:

From brain activities to mathematical models The TempUnit model, a study case for GPU computing in scientific computation.

What part of the brain?

How to study it ?

First attempt: use of a MLP What is a MLP?

First Attempt: MLP (2)

Results (1)

Results (2)

Crack the code !! Frequency code (Number of spikes in a time lap) ? Spatial coding (distributed trough the network) ? Temporal code (Precise binary pattern) ? Spatio-temporal code (Synchronies) ? Something else ?

The model x XtXt

Learn the parameters v i Solving a system of linear equation oversized. Much faster and straightforward than backpropagation for the MLP Example of a learned basis function

Performances compared to MLP

Check Chap. 12

Graph of Neuronal Activity The output activity of a TempUnit neural network can be described by a graph directly related to its connectivity – You determine the topology of your graph easily Allow to determine the input activity for a particular desired output

Can a real biological neuron do that ?

Pattern recognition

learning rules for unsupervised learning

EPSP from the integrate-and-fire model To find the position of the maximum (peak), one has to resolve the following equation: (Gerstner & Kistler, 2002)  From the integrate and fire, the α function: time

The new equation of the TempUnit model: With μ, the maximum value: time p=0 p=6 p : position of the synapse

From equations to a simulation software