1. > What Determine's a Neuron's Tuning? The Efficient Coding of Sensory Information Xavier.Rampino@mailhec.net 4January2011

2. 04/01/2011 Efficient Coding of Sensory Information 2 Summary Introduction The model Results Conclusion Bibliography

3. 04/01/2011 Efficient Coding of Sensory Information 3 1. Introduction The context : Problem of finding an efficient coding

4. 04/01/2011 Efficient Coding of Sensory Information 4 1. Introduction Whatmakes a coding efficient ? Preserves the underlyingsoundfeatures Lowest size possible for a givenquality Easy to encode and decode

7. We insert different white noises

8. We use filters to findmost probable spikes

10. We insert different white noises

11. We use filters to findmost probable spikes

13. Spikes are chosen to maximizeefficiency of the code (non-redudancy)

16. Efficient for a wide range of signals (both transient and harmonic)

17. Time-relative

19. Assuming that the kernel functions exist at all time points during the signal t :(1) sm(τ) = coefficient at time τ for φM ε(t) = additive noise

21. 04/01/2011 Efficient Coding of Sensory Information 13 2. The model

25. 04/01/2011 Efficient Coding of Sensory Information 18 2. The model Learning: Probabilisticform Wethen have, for each m : = Residualerrorat position tim of kernelφM We know and x^, sowithadditional computations wecandeduce an optimal value for φM

26. 04/01/2011 Efficient Coding of Sensory Information 19 Summary Introduction The model Results Conclusion Bibliography

27. 04/01/2011 Efficient Coding of Sensory Information 20 3. Results Because one of the condition wasthat the model had to berobust to a large range of accousticsignals, the training datasetwascomposed by : Mammalianvocalizations Nature sounds Ambient (rain, wind) Transient (crunchingleaves, impact of wood)

28. 04/01/2011 Efficient Coding of Sensory Information 21 3. Results Red : Model Blue : Physiological cat data The model predictsrevcor (physiological) shapes!

30. Blue : Physiological Cat Data

31. Black : EnvironmentalSoundsInitialization

32. Green : Animal VocalizationInitializationWith speech initialization, the model yieldssimilarresults as with the classicdataset.

33. 04/01/2011 Efficient Coding of Sensory Information 23 3. Results Comparison of the model initializedwithdifferentdatasets : Environmental Sound : Verybrief Vocalizations : Longer Reserved Speech : Reverse of classic model (Grey bars = 5 ms)

35. Light Blue : Not learning Model

36. Black : Fourier Transform

37. Blue : Daubechieswavelettransform

40. The kernelfunctionsweobtainedwith the right initialization (mixed naturalsounds) shouldbe good approximations of whathappens in a neuron « black box ».

42. Optimizingthis system is NP-Hard

43. Doesn’tdescribe the underlyingsoundfeatures

45. 04/01/2011 Efficient Coding of Sensory Information 29 5. Bibliography Bibliography : Evan Smith, Michael S. Lewicki, Efficient coding of time-relative structure usingspikes, Neural Computation January 2005, Vol. 17, No. 1: 19–45. Evan Smith, Michael S. Lewicki, Efficient auditorycoding, Nature 439, 978-982 (23 February 2006) Dario Ringach,Robert Shapley, Reverse correlation in neurophysiology (2003), Cognitive Science Mallat, S. G. & Zhang, Z. Matching pursuits with time–-frequency dictionaries. IEEE Trans. Signal Process. 41, 3397–-3415 (1993).

46. 04/01/2011 Efficient Coding of Sensory Information 30 Thankyou for your attention !