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

2. Purpose Compare computer model to biological system Compare computer model to biological system Evaluate utility of receptive fields Evaluate utility of receptive fields

3. 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

4. Neural network

5. Training set Different neurons trained on simple sounds Different neurons trained on simple sounds 0 Time (ms) 200 Frequency (kHz) 5 UpsweepsPure tonesDownsweeps

6. Training procedure Oja’s rule – modified Hebbian learning Oja’s rule – modified Hebbian learning Update rule: Update rule: Some particulars: Some particulars: Training set of 276 stimuli Training set of 276 stimuli 600 iterations through each stimulus 600 iterations through each stimulus

7. Results – weights 210 Frequency (Hz) 14501320 22601920 3075 2650 3850 3460 4400 4270 5510

8. Generation of STRFs Create ripple stimuli by equation: Create ripple stimuli by equation:

9. Generation of STRFs cont. Feed ripple into model and get output Feed ripple into model and get output Fit curve to output Fit curve to output

10. Generation of STRFs cont. Magnitude and phase shift become one pixel of transfer function: Magnitude and phase shift become one pixel of transfer function:

11. Generation of STRFs cont. 2D inverse Fourier transform to obtain STRF 2D inverse Fourier transform to obtain STRF Pad matrix with zeros to get smoother image Pad matrix with zeros to get smoother image Use similar color schemes as literature Use similar color schemes as literature

12. Results - STRFs

13. Comparison to tuning curves Depicts response intensity to different freqs. Depicts response intensity to different freqs. Peak of curve = best frequency (BF) Peak of curve = best frequency (BF)

14. Comparison of BFs Peak of tuning curve compared to max value of STRF Peak of tuning curve compared to max value of STRF

15. Temporal component Line of best fit around max area Line of best fit around max area Different slopes means different temporal characteristics Different slopes means different temporal characteristics

16. Temporal component cont.

17. Discussion & Conclusion Real STRFs look similar to computational STRFs Real STRFs look similar to computational STRFs Means underlying processing is also similar Means underlying processing is also similar