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A Dynamical Pattern Recognition Model of Gamma Activity in
Auditory Cortex
A Dynamical Pattern Recognition Model of Gamma Activity in
Auditory Cortex
Will PennyWill Penny
Wellcome Trust Centre for Neuroimaging,Wellcome Trust Centre for Neuroimaging,University College London, UKUniversity College London, UK
Centre for Integrative Neuroscience and Neurodynamics,University of Reading, 17th March 2010
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GammaActivity(>30Hz)
ComputationalNeuroscience
Imaging Neuroscience
SpikeTimeDependentPlasticity
BOLDsignal
BindingProblem
Localisation
Miller, PLOS-CB,2009
Goense et alCurr Biol, 2008
Singer,Neuron, 1999
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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
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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
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Bandpass filters
Allen (1985) Cochlear Modelling IEEE ASSP
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Onset Cells, Offset Cells, etc.
Hromadka et al, PLOS B, 2008Cell attached recordings from A1 in rat
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Onset and offset cells with frequency adaptation
Hromadka et al, PLOS B, 2008
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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
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Spike Time Dependent Plasticity
Abbott and Nelson. Nat Neuro, 2000
Pre
Post
25ms
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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 ….
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Hopfield and Brody, PNAS 2001
Hopfield Brody Model
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0.2 0.4 0.6 0.8 1-0.5
0
0.5Audio waveform
time (s) time (s)
freq
uenc
y (H
z)
Audio spectrogram
0.2 0.4 0.6
1000
2000
3000
0.2 0.4 0.6 0.8 10
50
time (s)
Spikes
0.2 0.4 0.6 0.8 10
1
time (s)
In Currents
0.2 0.4 0.6 0.8 1
0.20.40.60.8
time (s)
LFP
time (s)
freq
uenc
y (H
z)
LFP Spectrogram
0 0.5 1
20
40
60
80
“Add”
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0.2 0.4 0.6 0.8 1-0.5
0
0.5Audio waveform
time (s) time (s)
freq
uenc
y (H
z)
Audio spectrogram
0.2 0.4 0.6
1000
2000
3000
0.2 0.4 0.6 0.8 10
50
time (s)
Spikes
0.2 0.4 0.6 0.8 10
1
time (s)
In Currents
0.2 0.4 0.6 0.8 1
0.4
0.6
time (s)
LFP
time (s)
freq
uenc
y (H
z)LFP Spectrogram
0 0.5 1
2040
60
80
“Add”
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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
Frequencies near recognition time point
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Sparse Coding
Hromadka et al, PLOS B, 2008
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fb
tb
k=1 k=2
j=1 j=1
j=2 j=2
Time
Frequency
fmax
Spike Time Dependent Plasticity
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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
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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
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Levels of Analysis
Algorithmic level – what algorithm is being used ?
Similarity of Occurrence Times
Implementation level – how is it implemented ?
Transient Synchronisation
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Recognising Patterns
Bandpassfilteringat multiple spatial scales
Spatial configurationof Features
Leveldetection
Bandpassfilteringat multiple temporal scales
Temporal configurationof Features
Leveldetection
Fukushima, Rolls etc.
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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
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-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6-0.5
0
0.5
1
x
t
F (
Hz)
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50
1000
2000
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.60
100
200
300
f (H
z)Occurrence Times
y=[t1, t2, t3, ……, t45]
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-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6-0.5
0
0.5
1
x
t
F (
Hz)
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50
1000
2000
-0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.60
100
200
300
f (H
z)Autoregressive features
K frames
)()(...)2()1()( 21 teptxatxatxatx p EachFrame:
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1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
7
8
9
10
0
1
2
3
4
5
6
7
8
9
Confusion matrix from one cross-validation run
Truth
Cla
ssifi
catio
n
HB spokendigit database
FirstNearestNeighbourClassifier
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Occurrence TimesAutoregressive Parameters
Single shot learning results
Results on single subject
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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
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ECOG recordings over fronto-temporal cortex
Canolty et al. Front. Neuro, 2007
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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.
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Time (ms)
Fre
quen
cy (
Hz)
Words
-200 0 200 400 600 80060
80
100
120
140
160
2.5
3
3.5
4
4.5
5
5.5
Time (ms)
Fre
quen
cy (
Hz)
NonWords
-200 0 200 400 600 80060
80
100
120
140
160
2.5
3
3.5
4
4.5
5
5.5
Time (ms)
Fre
quen
cy (
Hz)
Difference
-200 0 200 400 600 800
20
40
60
80
100
120
140
160
-0.2
0
0.2
0.4
0.6
0.8
Time (ms)
Fre
quen
cy (
Hz)
T-score
-200 0 200 400 600 800
20
40
60
80
100
120
140
160
-2
0
2
4
6
8
10
12
14
ECOG recordings over STS
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u y
x
u yu y
Encoding
Symmetric
Decoding
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u yStimulusLabels
LocalField
x
tk
s
f(x,t)
AuditoryInput Pattern
BandpassFilters
Onset, offsetand peak responsetimes
Input-dependentFrequencies
Weakly-coupledOscillators Pattern
Recognition
FeatureExtraction
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WCO-TS Model
)]()(sin[),()( ttwtxft ijj
ijii
Input-pattern dependent frequencies
LateralConnectivity:Uniform (A)
Sync is stablestate
j
j tty )(cos)(
LFP
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WCO-TS
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
20
40
60
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 20
20
40
60
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2-20
0
20
f(x,t)
cos (t)
y(t)
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fb
tb
k=1 k=2
j=1 j=1
j=2 j=2
Time
Frequency, fi(x,t)
fmax
Minimal model with four parameters: ={A, fmax, fb, tb }
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Time (ms)
Fre
quen
cy (
Hz)
Words
-200 0 200 400 600 80060
80
100
120
140
160
2.5
3
3.5
4
4.5
5
5.5
Time (ms)
Fre
quen
cy (
Hz)
NonWords
-200 0 200 400 600 80060
80
100
120
140
160
2.5
3
3.5
4
4.5
5
5.5
Time (ms)
Fre
quen
cy (
Hz)
Difference
-200 0 200 400 600 800
20
40
60
80
100
120
140
160
-0.2
0
0.2
0.4
0.6
0.8
Time (ms)
Fre
quen
cy (
Hz)
T-score
-200 0 200 400 600 800
20
40
60
80
100
120
140
160
-2
0
2
4
6
8
10
12
14
ECOG recordings over STS
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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 96trials look at response to wordversus nonword
Computational saving:only look at those cells activated by both word and nonword stimuli
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Model fitting
• EN: ECOG spectra-model spectra
• EC: Word versus non word
)exp()|( ECENyp
Uniform priors, Metropolis Hastings estimation of posterior densities
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Training Testing
Minimal Model
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Testing Training
Augmented Model
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SummaryLevels 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.
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GammaActivity(>30Hz)
ComputationalNeuroscience
Imaging Neuroscience
SpikeTimeDependentPlasticity
BOLDsignal
BindingProblem
Localisation
Miller, PLOS-CB,2009
Goense et alCurr Biol, 2008
Singer,Neuron, 1999
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Melissa Zavaglia & Mauro UrsinoDEIS, 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
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time (s)
freq
uenc
y (H
z)
Word 6, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 2
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 3
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 4
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 5
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 6
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 7
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 8
0 0.5 1
20406080
time (s)fr
eque
ncy
(Hz)
Word 6, Ex 9
0 0.5 1
20406080
Sensitivity
FN
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time (s)
freq
uenc
y (H
z)
Word 1, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 2, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 3, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 4, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 5, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 6, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 7, Ex 1
0 0.5 1
20406080
time (s)
freq
uenc
y (H
z)
Word 8, Ex 1
0 0.5 1
20406080
time (s)fr
eque
ncy
(Hz)
Word 9, Ex 1
0 0.5 1
20406080
Specificity
FP
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Optical Imaging
Harrison et al. Acta Oto, 2000