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Visuelle Kodierung

Visuelle Kodierung. Christian B. Mendl. Unabhängige Nachwuchsgruppe „Visuelle Kodierung“ am Max-Planck-Institut für Neurobiologie unter Tim Gollisch. Ziele der Arbeitsgruppe. Untersuchung der Sehreizverarbeitung und ‐Kodierung in der Retina (Augennetzhaut)

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Visuelle Kodierung

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  1. Visuelle Kodierung Christian B. Mendl Unabhängige Nachwuchsgruppe „Visuelle Kodierung“ am Max-Planck-Institut für Neurobiologie unter Tim Gollisch

  2. Ziele der Arbeitsgruppe • Untersuchung der Sehreizverarbeitung und ‐Kodierung in der Retina (Augennetzhaut) • Grundlagenforschung für zukünftige Netzhaut-Implantate • Retina experimentell gut zugängliches Nervenzellsystem; Modell für neuronale Kodierung

  3. Schematischer Aufbau der Retina Heinz Wässle, Parallel Processing in theMammalian Retina Nature Reviews Neuroscience, Vol 5, October 2004 Säugetier-Retina (schematisch): esgibtsechsKlassen von Neuronen in derSäugetier-Retina: Stäbchen (1), Zapfen (2), Horizontalzellen (3), Bipolarzellen (4), Amakrinzellen (5) und retinaleGanglionzellen (6).

  4. Experimentelles Messprinzip

  5. Spike-TriggeredAverage (STA) Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Computation in Neural Systems, 2001, 12, 199–213

  6. Spike-TriggeredAverage (cont.) Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Computation in Neural Systems, 2001, 12, 199–213

  7. Spike-TriggeredAverage (cont.) Chichilnisky, E. J. A simple white noise analysis of neuronal light responses. Computation in Neural Systems, 2001, 12, 199–213

  8. PairwiseCorrelations • Significant interactions between neurons in the vertebrate retina • Exponential increase in number of possible collective states • Simplifying hypotheses required to effectively capture network statistics • Ising model taking into account pairwise interactions only gives quantitatively good results EladSchneidman, Michael J. Berry II, Ronen Segev and William Bialek. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 2006 doi:10.1038/nature04701

  9. Analysis Setup Simultaneous responses of 40 retinal ganglion cells in the salamander to a natural movie clip. Each dot represents the time of an action potential.

  10. Failure of the Independent Approximation Probability distribution of synchronous spiking events approximates an exponential Occurrence rate predicted if all cells were independent Distribution of synchronous events after shuffling each cell’s spike train to eliminate all correlations, compared to the Poisson distribution

  11. Ising Model for Pairwise Interactions EladSchneidman, Susanne Still, Michael J. Berry and William Bialek. Network Information and Connected Correlations. Phys. Rev. Lett., December 2003. Maximum entropy principle used for parameter fitting Interaction strength Jij plotted against the correlation coefficient Cij

  12. Pairwise Interactions Approximationis Quantitatively Sufficient Maximum entropy model taking into account all pairwise correlations Independent model Fraction of full network correlation (measured by the multi-information IN) in 10-cell groups that is captured by the maximum entropy model of second order, I(2)/IN

  13. Interactions and Local Fields in Networks of Different Size Density map of effective interaction fields experienced by a single cell versus its own bias or local field Mean interactions Jij and local fields hi describing groups of N cells Pairwise interaction in a network of 10 cells Jij(10) plotted against the interaction values of the same pair in a subnetwork containing only 5 cells Jij(5)

  14. Extrapolation to Larger Networks Average independent cell entropy S1 and network multi-information In (true entropy equals SN=S1-IN) Information that N cells provide about the activity of cell N+1 Examples of ‘check cells’: the probability of spiking is an almost perfectly linear encoding of the number of spikes generated by the other cells

  15. Conclusions (Pairwise Interactions) • Summary: pairwise interactions sufficient to explain network statistics • Critical annotation: correlations due to stimuli not taken into account • Results confirmed by a very similar paper by Shlens et.al. avoiding external stimulation; moreover, interactions limited to adjacent cells Jonathon Shlens et.al. The Structure of Multi-Neuron Firing Patterns in Primate Retina. The Journal of Neuroscience, 2006, 26(32)

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