1. 2nd TAC Meeting Christian B. Mendl Tim Gollisch Lab Neuronal Coding in the Retina and Fixational Eye Movements Neuronal Coding in the Retina and Fixational Eye Movements 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) d)c)

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