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

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

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

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

Bandpass filters Allen (1985) Cochlear Modelling IEEE ASSP

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

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

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

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

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 ….

Hopfield and Brody, PNAS 2001 Hopfield Brody Model

“Add”

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

Sparse Coding Hromadka et al, PLOS B, 2008

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

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

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

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

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.

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

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

Autoregressive features K frames Each Frame:

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

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

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

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

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.

ECOG recordings over STS

uy  x uy uy Encoding Symmetric Decoding

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

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

WCO-TS f(x,t) cos  (t) y(t)

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 }

ECOG recordings over STS

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

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

Training Testing Minimal Model

Testing Training Augmented Model

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.

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

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

Sensitivity FN

Specificity FP

Optical Imaging Harrison et al. Acta Oto, 2000