1 / 17

Assignment 2: reverse correlation

Assignment 2: reverse correlation. Outline. The assignment requires you to Write code to produce graphs Make observations from the graphs Draw conclusions. Coding. Coding is in MATLAB. I will provide you with templates that provide you with: A list of MATLAB functions to use

karl
Download Presentation

Assignment 2: reverse correlation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Assignment 2: reverse correlation

  2. Outline • The assignment requires you to • Write code to produce graphs • Make observations from the graphs • Draw conclusions

  3. Coding • Coding is in MATLAB. • I will provide you with templates that provide you with: • A list of MATLAB functions to use • Comments describing the flow of operations

  4. Some Coding Tips • It is important that you know how to use the debugger. • Use the MATLAB Help facility. • You should generally never have a loop (or nested loop) that involves more than a few hundred iterations.

  5. Dataset • We will be using a portion of the Neural Prediction Challenge Dataset • Responses of V1 neurons to natural vision movies in awake behaving macaque • Both neural responses and visual stimuliare provided • Available at http://neuralprediction.berkeley.edu/ • But you can download the files you need from the course website. We will be analyzing a particular neuron (R0221B)

  6. Submission Details • You will submit a short lab report on your experiments. • For each experiment, the report will include: • The code you developed • The graphs you produced • The observations you made • The conclusions you drew

  7. Graphs • The graphs you produce should be as similar as possible to mine. • Make sure everything is intelligible!

  8. Due Date • The report is due Wed Mar 23

  9. Reverse Correlation • Raw stimulus response cross-correlation: • Now represent the kernel h as an mxTmatrix, where • Correction for temporal stimulus bias: • Correction for spatial stimulus bias: • But this doesn’t work, because there are too many coefficients in Qss to estimate, and too little power in the high frequencies of the stimulus to estimate them.

  10. Solution: Regularized Inverse • Use SVD decomposition: • Where U and V are orthonormal rotation matrices and S is a diagonal scaling matrix carrying the eigenvalues of Qss • The eigenvalues represent the power of the autocorrelation in each of the underlying principle directions (eigenvectors).

  11. Regularized Inverse • Once the SVD decomposition is computed, taking the inverse is easy. • However, this inverse is unreliable, because noisy eigenvalues in S near 0 result in large noisy values in S-1. • To avoid this, only take the largest eigenvalues from S, and set the remaining diagonal elements of S-1 to 0.

  12. Firing Rates Histogram KDE

  13. Stimulus-Response Cross-Correlation

  14. First-Order Temporal Autocorrelation of Stimulus

  15. STRF Corrected for Temporal Bias of Stimulus

  16. Unregularized Correction for Spatial Bias of Stimulus

  17. Regularized Correction for Spatial Bias of Stimulus

More Related