2nd TAC Meeting

1. 2nd TAC Meeting Neuronal Coding in the Retina and Fixational Eye Movements Christian B. Mendl Tim Gollisch Lab April 22, 2010

2. Outline • Review of last TAC meeting • Informative spike response features • Latency coding by cell pairs • Modeling response features • Outlook

3. Review of Last TAC Meeting • Fixational eye movements, microsaccades • Counteract visual perception fading • Enhancement of spatial resolution • Last TAC meeting: information theory: mutual information, synergy → use as screening tool • To-do: • stimulus variation: grating instead of border • neuronal model building • decoding strategies

4. Informative Spike Response Features Observed spike responses of a single cell

5. Various Response Types a) b) c) d)

6. Informative Spike Features (cont) Observed spike responses of a single cell

7. Informative Spike Responses:Number of Spikes/Trial

8. Informative Spike Responses:Internal Structure ISI (inter-spike-interval)

9. Informative Spike Responses:Latency

10. Latency Coding by Cell Pairs • Latency emerges as most informative spike response feature • Timing reference? (Brain doesn’t know stimulus onset) • → Need several cells

11. Cell Pairs: Experimental Data

12. Relative Latency time intervals accessible to readout by higher brain regions

13. Cell Pairs: Latency Scatter Plot K-means clustering: relative weight of off-diagonal elements: 19.1%

14. Global Drift Correction

15. Drift-Corrected Latency Scatter Plot K-means clustering: relative weight of off-diagonal elements: 9.6%

16. Latency Correlations • Observation: global latency drift leads to (artificial) correlations, can correct for that • Question: cells internally interacting on short-term scale? • → Compare spikes shuffled by one trial

17. Latency Correlations (cont) shuffled version: no correlations

18. Latency Correlation Statistics

19. Conclusions Latency Coding • Use latency instead of spike count and inter-spike-interval • High information content in latency data from two cells • Correlations might improve coding

20. Comparison with LN Models

21. LN Models (cont)

22. Conclusions Modeling • Qualitative agreement • But still much room for improvement, latency data on 10 ms scale not reproduced • Gain control might be able to reproduce experimental spike histogram

23. Outlook • Fixational eye movements have been reported in Salamander • But precise quantification still missing • → Search coil setup