Neuronal Coding in the Retinaand Fixational Eye Movements

Christian Mendl, Tim Gollisch

Max Planck Institute of Neurobiology, Junior Research Group Visual Coding

Overview

So-called “fixational eye movements” are an important feature of normal human vision since they counteract visual perception fading and enhance spatial resolution. Yet it is not yet fully understood how they influence neuronal coding schemes. To investigate these questions, we record the action potential of amphibian retinal ganglion cells, mimicking fixational eye movements by oscillatory shifts of the stimulus.

Extracellular recordings from retinal ganglion cells using a MEA (Multi-Electrode-Array)

Latency Coding and Correlations

Relative latency → time intervals accessible by higher brain regions

Global drift correction

• Informative spike response features?

• Role of correlations?

• Population code?

Timing histogram of first spike in each trial

Stimuli from Rucci et al., Miniature eye movements enhance fine spatial detail

• Latency emerges as most informative spike response feature

• Timing reference? (Brain doesn’t know stimulus onset)

• → Need several cells

Single cell responses for different orientations (color coded)

Spike responses of two cells (blue and green, respectively)

Subtracting global drift reveals internal correlations

All experiments are performed on Axolotl and Frog (Xenopus laevis)

Concrete task: discriminate 5 different orientations based on the spike responses of retinal ganglion cells

Latency correlation statistics for several cell pairs

Shuffling trials

References

• Meister et al. (1995), Concerted signaling by retinal ganglion cells. Science 270

• T. Gollisch and M. Meister (2008), Rapid neural coding in the retina with relative spike latencies. Science 319

• S. Martinez-Conde et al. (2006), Microsaccades counteract visual fading during fixation. Neuron 49

• M. Greschner et al. (2002), Retinal ganglion cell synchronization by fixational eye movements improves feature estimation. Nature Neuroscience 5

• M. Rucci et al. (2007), Miniature eye movements enhance fine spatial detail, Nature 447

• M.J. Schnitzer and M. Meister (2003), Multineuronal firing patterns in the signal from eye to brain. Neuron 37

• E. Schneidman et al. (2003), Synergy, redundancy, and independence in population codes. Journal of Neuroscience 23(37)

• D.K. Warland et al. (1997), Decoding visual information from a population of retinal ganglion cells. Journal of Neurophysiology 78

Summary

• Latency emerges as the most informative spike response feature

• Relative spike timings of two cells contain information and are directly accessible to readout by higher brain regions

• Responses of cell pairs are correlated → evidence for coding structure via intrinsic interactions

• Receptive field position on grating could predict response latency

Search for Internal Mechanisms

Employing linear phase shifts (color coded). Each pair of ellipses shows the receptive field position relative to the oscillating grating

Relative response latencies for different phase shifts

Spike response raster plot for a single cell

• Relationship between a cell’s receptive field position on the grating and response latency?

• → Replace orientations by linear phase shifts for easier analysis

Spike count histogram

• Response latency matches phase shift and follows reversal of the oscillatory movement direction

• Latency range bigger than stimulus movement time interval

• First spike is elicited earlier when receptive field moves from a bright to a dark region

The upper stimulus modality imitates oscillatory eye movement, and the lower microsaccades

Compare with latency correlations after shuffling

Latency scatter plot

Conclusion: there are cell pairs showing internal correlations, additional to global drift effects

• Observation: latencies are correlated