pptx - TAC Meeting


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  • First I want to explain fixational eye movements, then formulate my research question and research strategy. I will briefly sketch the experimental setup in case you don‘t remember all the details from Vidhya‘s Friday seminar talk.
  • First I want to briefly explain fixational eye movements. The picture to the right illustrates these eye movements as measured for a human eye. (Click) Fixational eye movements are a constant feature of normal vision, present even when „fixing“ the gaze on an object – hence the name “fixational eye movements“. Importantly, the visual perception fades away when the image of an object is stabilized artificially on the retina. Enhancement of spatial details and improvement of stimulus feature estimation: distinquish between grating orientation… So these movements are an integral part of normal vision.
  • To quantify the effects of these eye movements on neuronal coding, Martin Greschner and colleagues used video-oculography on turtles. They isolated a periodic component (Click) at approximately 5 Hz. The corresponding amplitude was relatively small, namely 5µm on the retina, which corresponds to the diameter of a photoreceptor. The authors concluded that ... This could potentially improve stimulus feature estimation by the brain.
  • First point: How should the brain interpret the responses? Greschner found synchronized firing -> population code
  • My research strategy derives from the fact that fixational eye movements result in oscillatory shifts of the image on the retina. Imitate these eye movements by a shifting black-white border. Green ellipses denote the receptive fields of 2 ganglion cells; blue arrow shows the shifting border orientation
  • Each dot represents a spike. First idea: work on single cell level, use e.g. spiking rate. But rate approximately constant for both stimuli. Have to use a much higher amplitude as compared to Martin Greschner to elicit any responses.
  • Quantify these correlations by a histogram plot of relative spike timings. Spike timing cross-correlations can provide information about the stimulus; relative spike timings are different for the two stimuli
  • So I created these histogram plots for measured spike timings, as shown in the figure; the 5 angles are color-coded. One observes that the correlations are periodic, showing the same period as the stimulus. Green curve: peak at zero: cells tend to spike synchronously. Red curve: delay.
  • Apply information theory... Upper right: „different patterns for different stimuli?“
  • Quantify population responses by information theory measures. Imutual: stimulus: 5 different angles, spike patterns; either of single cells or cell pairs. Synergy can be positive or negative.
  • Imutual (stimlocked) > Imutual (unlocked), and „additional information“ in stimulus locked case smaller
  • pptx - TAC Meeting

    1. 1. Neuronal Coding in the Retina and Fixational Eye Movements Friday Seminar Talk November 6, 2009 Christian Mendl Tim Gollisch Lab
    2. 2. Outline • Experimental setup • Review of fixational eye movements • Research questions and strategy • A look at the observed data • Spike timing cross-correlations • Information theory: entropy, mutual information, synergy, ... • Summary and outlook
    3. 3. ganglion cells Experimental Setup
    4. 4. Fixational Eye Movements source: Martinez-Conde laboratory • Constant feature of normal vision • Visual perception fading • Enhancement of spatial resolution Riggs LA and Ratliff F. The effects of counteracting the normal movements of the eye. Journal of the Optical Society of America (1952) Ditchburn RW and Ginsborg BL. Vision with a stabilized retinal image. Nature (1952) Meister M, Lagnado L and Baylor DA. Concerted signaling by retinal ganglion cells. Science (1995) Martinez-Conde S et al. Microsaccades counteract visual fading during fixation. Neuron (2006)
    5. 5. Fixational Eye Movements II Eye movements of the turtle during fixation • Periodic component at approximately 5 Hz • Imitating fixational eye movements → retina better encoder • Neurons synchronize more Greschner, Ammermüller et.al. Nature Neuroscience (2002)
    6. 6. Research Questions • How can the brain discriminate between various stimuli in the context of fixational eye movements? Optimal decoding strategy? • Synchronized responses of several retinal ganglion cells → population code?
    7. 7. Research Strategy Concrete task: based on spike responses, discriminate 5 different angles
    8. 8. Observed Data stimulus period: 800 ms
    9. 9. Spike Timing Cross-Correlations
    10. 10. Spike Timing Cross-Correlations II stimulus period
    11. 11. Encoding the Spike Train stimulus-locked binning unlocked binning Encoding spike patterns → observer knows the stimulus phase
    12. 12. Information Theory Mutual information Imutual → How much information („bits“) do the spikes contain about the stimulus Synergy → How much additional information is contained in the simultaneous activity of two cells as compared to the individual cells’ responses
    13. 13. Mutual Information unlocked binning stimulus-locked binning individual cells cell pairs
    14. 14. Population Code: Synergy Synergy versus mutual information for several recordings unlocked binning stimulus-locked binning
    15. 15. Summary • Fixational eye movements provide information about the stimulus • If the brain uses individual cells, it needs to know the phase of the fixational eye movements • For multiple cells, the phase information becomes less important since the cells are synergistic
    16. 16. Outlook • Effect of shorter stimulus periods and smaller amplitudes? • Try different decoding stategies: optimal patterns, bin sizes?
    17. 17. Acknowledgements Tim Gollisch Lab • Tim Gollisch • Daniel Bölinger • Vidhya Krishnamoorthy Thesis Advisory Board • Tim Gollisch • Erwin Frey (LMU) • Andreas Herz • Günther Zeck Boehringer Ingelheim Fonds Foundation for Basic Research in Medicine