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2nd TAC Meeting. Neuronal Coding in the Retina and Fixational Eye Movements. Christian B. Mendl Tim Gollisch Lab. April 22, 2010. Outline. Review of last TAC meeting Informative spike response features Latency coding by cell pairs Modeling response features Outlook. - PowerPoint PPT Presentation
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2nd TAC Meeting
Christian B. MendlTim Gollisch Lab
Neuronal Coding in the Retinaand Fixational Eye Movements
April 22, 2010
Outline
• Review of last TAC meeting• Informative spike response
features• Latency coding by cell pairs• Modeling response features• Outlook
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
Informative Spike Response Features
Observed spike responses of a single cell
Various Response Typesa) b)
d)c)
Informative Spike Features (cont)Observed spike responses of a single cell
Informative Spike Responses:Number of Spikes/Trial
Informative Spike Responses:Internal Structure
ISI (inter-spike-interval)
Informative Spike Responses:Latency
Latency Coding by Cell Pairs
• Latency emerges as most informative spike response feature
• Timing reference? (Brain doesn’t know stimulus onset)
• → Need several cells
Cell Pairs: Experimental Data
Relative Latencytime intervals accessible to readout by higher brain regions
Cell Pairs: Latency Scatter Plot
K-means clustering: relative weight of off-diagonal elements:19.1%
Global Drift Correction
Drift-Corrected Latency Scatter Plot
K-means clustering:relative weight ofoff-diagonal elements:9.6%
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
Latency Correlations (cont)
shuffled version: no correlations
Latency Correlation Statistics
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
Comparison with LN Models
LN Models (cont)
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
Outlook
• Fixational eye movements have been reported in Salamander
• But precise quantification still missing• → Search coil setup