Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

17
Analysis of spectro- Analysis of spectro- temporal receptive temporal receptive fields in an fields in an auditory neural auditory neural network network Madhav Nandipati Madhav Nandipati

description

Background STRF describes properties in frequency and time domains STRF describes properties in frequency and time domains Have been studied in many animals but not adequately in a computer model Have been studied in many animals but not adequately in a computer model

Transcript of Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Page 1: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Analysis of spectro-Analysis of spectro-temporal receptive temporal receptive fields in an auditory fields in an auditory

neural networkneural networkMadhav NandipatiMadhav Nandipati

Page 2: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

PurposePurpose Compare computer model to Compare computer model to

biological systembiological system Evaluate utility of receptive fieldsEvaluate utility of receptive fields

Page 3: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

BackgroundBackground STRF describes properties in STRF describes properties in

frequency and time domainsfrequency and time domains Have been studied in many animals Have been studied in many animals

but not adequately in a computer but not adequately in a computer modelmodel

Page 4: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Neural networkNeural network

Page 5: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Training setTraining set Different neurons trained on simple Different neurons trained on simple

soundssounds

0 Time (ms) 200 0 Time (ms) 2000 Time (ms) 200

Freq

uenc

y (k

Hz)

5

Freq

uenc

y (k

Hz)

5

Freq

uenc

y (k

Hz)

5

Upsweeps Pure tones Downsweeps

Page 6: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Training procedureTraining procedure Oja’s rule – modified Hebbian Oja’s rule – modified Hebbian

learninglearning Update rule:Update rule:

Some particulars:Some particulars: Training set of 276 stimuliTraining set of 276 stimuli 600 iterations through each stimulus600 iterations through each stimulus

Page 7: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Results – weightsResults – weights

210 Frequency (Hz) 14501320 22601920 3075

2650 38503460 44004270 5510

Page 8: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Generation of STRFsGeneration of STRFs Create ripple stimuli by equation:Create ripple stimuli by equation:

Page 9: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Generation of STRFs Generation of STRFs cont.cont.

Feed ripple into model and get Feed ripple into model and get outputoutput

Fit curve to outputFit curve to output

Page 10: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Generation of STRFs Generation of STRFs cont.cont.

Magnitude and phase shift become Magnitude and phase shift become one pixel of transfer function:one pixel of transfer function:

Page 11: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Generation of STRFs Generation of STRFs cont.cont.

2D inverse Fourier transform to 2D inverse Fourier transform to obtain STRFobtain STRF

Pad matrix with zeros to get Pad matrix with zeros to get smoother imagesmoother image

Use similar color schemes as Use similar color schemes as literatureliterature

Page 12: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Results - STRFsResults - STRFs

Page 13: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Comparison to tuning Comparison to tuning curvescurves

Depicts response intensity to Depicts response intensity to different freqs.different freqs.

Peak of curve = best frequency (BF)Peak of curve = best frequency (BF)

Page 14: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Comparison of BFsComparison of BFs Peak of tuning curve compared to Peak of tuning curve compared to

max value of STRFmax value of STRF

Page 15: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Temporal componentTemporal component Line of best fit around max areaLine of best fit around max area Different slopes means different Different slopes means different

temporal characteristicstemporal characteristics

Page 16: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Temporal component Temporal component cont.cont.

Page 17: Analysis of spectro-temporal receptive fields in an auditory neural network Madhav Nandipati.

Discussion & ConclusionDiscussion & Conclusion Real STRFs look similar to Real STRFs look similar to

computational STRFscomputational STRFs Means underlying processing is also Means underlying processing is also

similarsimilar