2. A Dynamical Pattern Recognition Model of Gamma Activity in Auditory Cortex Will Penny Wellcome Trust Centre for Neuroimaging, University College London, UK Centre for Integrative Neuroscience and Neurodynamics, University of Reading, 17 th March 2010

3. Gamma Activity (>30Hz) Computational Neuroscience Imaging Neuroscience Spike Time Dependent Plasticity BOLD signal Binding Problem Localisation Miller, PLOS-CB,2009 Goense et al Curr Biol, 2008 Singer, Neuron, 1999

4. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

5. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

6. Bandpass filters Allen (1985) Cochlear Modelling IEEE ASSP

7. Onset Cells, Offset Cells, etc. Hromadka et al, PLOS B, 2008 Cell attached recordings from A1 in rat

8. Onset and offset cells with frequency adaptation Hromadka et al, PLOS B, 2008

9. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

10. Spike Time Dependent Plasticity Abbott and Nelson. Nat Neuro, 2000 Pre Post 25ms

11. Neuronal Synchronization Frequencies need to be similar (Kuramoto) With fast connections excitation causes sync (Gap Junctions) With slow connections inhibition causes sync (Chemical synapses, PING) Hopfield Brody – balanced excitation and inhibition See Bartos, Van Vreeswijk, Ermentrout, Traub ….

12. Hopfield and Brody, PNAS 2001 Hopfield Brody Model

13. “Add”

15. Word 1Word 2 30 frequency bands x 3 types (onset,offset,peak) x 10 adaptation times=900 cells Each word coded for by activity in an ensemble of 45 cells Frequencies near recognition time point

16. Sparse Coding Hromadka et al, PLOS B, 2008

17. fbfb tbtb k=1 k=2 j=1 j=2 Time Frequency f max Spike Time Dependent Plasticity

18. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

19. Levels of Analysis Algorithmic level – what algorithm is being used ? Example – Fourier Analysis Implementation level – how is it implemented ? Example – FFT if you have digital storage, adders, multipliers Holography if you have a laser and photographic plates See eg. Marr and Poggio, AI Memo, 1976

20. Levels of Analysis Algorithmic level – what algorithm is being used ? Similarity of Occurrence Times Implementation level – how is it implemented ? Transient Synchronisation

21. Recognising Patterns Bandpass filtering at multiple spatial scales Spatial configuration of Features Level detection Bandpass filtering at multiple temporal scales Temporal configuration of Features Level detection Fukushima, Rolls etc.

22. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

23. Occurrence Times y=[t1, t2, t3, ……, t45]

24. Autoregressive features K frames Each Frame:

25. Confusion matrix from one cross-validation run Truth Classification HB spoken digit database First Nearest Neighbour Classifier

26. Occurrence Times Autoregressive Parameters Single shot learning results Results on single subject

27. Overview Bandpass filtering and feature detection Spike Frequency Adaptation Spike Time Dependent Plasticity Neuronal Synchronization Sparse Coding Hopfield-Brody Model Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels

28. ECOG recordings over fronto-temporal cortex Canolty et al. Front. Neuro, 2007

29. Subject listened to words and “nonwords” Task: press button if word is a persons name (this data not analysed) Each nonword was created by taking a word, computing the spectrogram and removing ripple sound components corresponding to formants. The spectrogram was then inverse transformed (Singh and Theunissen, 2003) Each nonword matched one of the words in duration, intensity, power spectrum, and temporal modulation.

30. ECOG recordings over STS

31. uy  x uy uy Encoding Symmetric Decoding

32. u y Stimulus Labels Local Field x  tktk s f(x,t) Auditory Input Pattern Bandpass Filters Onset, offset and peak response times Input-dependent Frequencies Weakly-coupled Oscillators Pattern Recognition Feature Extraction

33. WCO-TS Model Input-pattern dependent frequencies Lateral Connectivity: Uniform (A) Sync is stable state LFP

34. WCO-TS f(x,t) cos  (t) y(t)

35. fbfb tbtb k=1 k=2 j=1 j=2 Time Frequency, f i (x,t) f max Minimal model with four parameters:  ={A, f max, f b, t b }

36. ECOG recordings over STS

37. Word 30 frequency bands x 3 types (onset,offset,peak) x 10 adaptation times=900 cells Each word coded for by activity in an ensemble of 45 cells Frequencies near recognition time point For each of 96 trials look at response to word versus nonword Computational saving: only look at those cells activated by both word and nonword stimuli

38. Model fitting EN: ECOG spectra-model spectra EC: Word versus non word Uniform priors, Metropolis Hastings estimation of posterior densities

40. Training Testing Minimal Model

41. Testing Training Augmented Model

42. Summary Levels of Analysis Occurrence Times for Speech Recognition Forward model of imaging data and stimulus labels FUTURE: Lack of direct evidence for TS mechanism Can STDP rules synchronize multiple spikes ? Have modelled activity in single region only.

43. Gamma Activity (>30Hz) Computational Neuroscience Imaging Neuroscience Spike Time Dependent Plasticity BOLD signal Binding Problem Localisation Miller, PLOS-CB,2009 Goense et al Curr Biol, 2008 Singer, Neuron, 1999

44. Melissa Zavaglia & Mauro Ursino DEIS, Cesena, Italy LIF network simulations Rich Canolty & Bob Knight, UCSF, San Francisco. ECOG data and analysis Tom Schofield & Alex Leff, UCL, London. Cognitive neuroscience of language Acknowledgements

45. Sensitivity FN

46. Specificity FP

47. Optical Imaging Harrison et al. Acta Oto, 2000