A Dynamical Pattern Recognition Model of Gamma Activity in Auditory Cortex

1. 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, 17th March 2010

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

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

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

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

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

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

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

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

10. 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 ….

11. Hopfield Brody Model Hopfield and Brody, PNAS 2001

12. “Add”

13. “Add”

14. Frequencies near recognition time point Word 1 Word 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

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

16. Spike Time Dependent Plasticity Frequency k=2 k=1 fmax fb j=2 j=2 j=1 j=1 tb Time

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

18. Levels of Analysis Algorithmic level – what algorithm is being used ? Example – Fourier Analysis Implementationlevel – 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

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

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

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

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

23. Autoregressive features Each Frame: K frames

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

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

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

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

28. 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.

29. ECOG recordings over STS

30. Encoding Decoding Symmetric u y u y u y F x

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

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

33. WCO-TS f(x,t) cos f(t) y(t)

34. Frequency, fi(x,t) k=2 k=1 fmax fb j=2 j=2 j=1 j=1 tb Time Minimal model with four parameters: q={A, fmax, fb, tb }

35. ECOG recordings over STS

36. Frequencies near recognition time point Word 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 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

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

39. Testing Training Minimal Model

40. Testing Training Augmented Model

41. 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.

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

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

44. Sensitivity FN

45. Specificity FP

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