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

3. What part of the brain?

4. How to study it ?

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

6. First Attempt: MLP (2)

7. Results (1)

8. Results (2)

9. 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 ?

11. The model x XtXt

12. 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

13. Performances compared to MLP

14. Check Chap. 12

15. 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

16. Can a real biological neuron do that ?

19. Pattern recognition

21. learning rules for unsupervised learning

22. 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

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

24. From equations to a simulation software