September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Novel Model-based identification of retinal ganglion cell subunits
Author Affiliations & Notes
  • Nishal Shah
    Electrical Engineering, Stanford University, Stanford, California, United States
  • Nora Brackbill
    Physics, Stanford University, Stanford, California, United States
  • Alexandra Tikidji-Hamburyan
    Neurosurgery, Stanford University, Stanford, California, United States
    Opthalmology, Stanford University, Stanford, California, United States
  • Colleen Rhoades
    Bioengineering, Stanford University, Stanford, California, United States
  • Georges A Goetz
    Neurosurgery, Stanford University, Stanford, California, United States
    Electrical Engineering, Stanford University, Stanford, California, United States
  • Alexander Sher
    University of California, Santa Cruz, Santa Cruz, California, United States
    Pew Charitable Trust Scholarship in the Biomedical Sciences (A.S.), San Francisco, California, United States
  • Alan Litke
    University of California, Santa Cruz, Santa Cruz, California, United States
  • Liam Paninski
    Columbia University, New York, New York, United States
  • Eero Simoncelli
    Howard Hughes Medical Institute, Chevy chase (CDP), Maryland, United States
    New York University, New York, California, United States
  • EJ Chichilnisky
    Neurosurgery, Stanford University, Stanford, California, United States
    Opthalmology, Stanford University, Stanford, California, United States
  • Footnotes
    Commercial Relationships   Nishal Shah, None; Nora Brackbill, None; Alexandra Tikidji-Hamburyan, None; Colleen Rhoades, None; Georges Goetz, None; Alexander Sher, None; Alan Litke, None; Liam Paninski, None; Eero Simoncelli, None; EJ Chichilnisky, None
  • Footnotes
    Support  NSF Grant 1430348 (CRCNS)
Investigative Ophthalmology & Visual Science September 2016, Vol.57, No Pagination Specified. doi:
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    • Get Citation

      Nishal Shah, Nora Brackbill, Alexandra Tikidji-Hamburyan, Colleen Rhoades, Georges A Goetz, Alexander Sher, Alan Litke, Liam Paninski, Eero Simoncelli, EJ Chichilnisky; Novel Model-based identification of retinal ganglion cell subunits. Invest. Ophthalmol. Vis. Sci. 201657(12):.

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      © 2017 Association for Research in Vision and Ophthalmology.

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Abstract

Purpose : Visual signals from photoreceptors are pooled by bipolar cells, rectified, and pooled to generate responses of retinal ganglion cells (RGCs). This process creates “subunits” in the RGC receptive field (RF) that mediate responses to fine texture and movement. Because it is difficult to record from bipolar cells, computational methods to expose their function based on RGC recordings are valuable. A previous method (Freeman et. al., 2015) used a greedy algorithm that assumed non-overlapping subunits. Can we identify RGC subunits with a more general method?

Methods : Macaque parasol RGCs were recorded using 512-electrode arrays ex vivo. Responses were fitted with a model in which stimulus intensities are combined linearly to produce subunit activations, which are then exponentiated and summed to obtain the firing rate. For a given number of subunits, the likelihood of the model parameters is maximized by alternating between estimating subunit membership by soft-clustering the ensemble of stimuli preceding spikes, and estimating the linear weights associated with each subunit.

Results : Fitting the model to simulated data, using fewer fitted subunits than the simulated number of bipolar cells, produced inferred subunits that subsumed several simulated bipolar cell RFs completely. Hence, a complete characterization may require more data. Subunits estimated from RGC data were spatially localized and non-overlapping, as expected from bipolar inputs of a single type, even though these properties were not assumed in the model. Finally, a “null” stimulus was presented, orthogonal to the linear RF estimated from spike-triggered averaging. A linear model predicts no response to this stimulus, but RGCs exhibited clear temporal structure. The subunit model captured some of this structure (R2≈0.3).

Conclusions : A novel technique for estimating subunits of primate RGCs shows promising results in recovering putative bipolar cell spatial structure.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

 

Spike triggered average (left) and subunits inferred for a 4-subunit model (right), fitted to a 12-subunit model simulation (top) and to data from a parasol RGC (bottom). Dot colors indicate subunit membership in simulated cell.

Spike triggered average (left) and subunits inferred for a 4-subunit model (right), fitted to a 12-subunit model simulation (top) and to data from a parasol RGC (bottom). Dot colors indicate subunit membership in simulated cell.

 

Rasters of responses to “null” stimulus from a RGC (middle), a fitted linear model (bottom), and a fitted 4-subunit model (top). The firing rate of each model was adjusted by an arbitrary scale factor to approximately match the raster structure in the data.

Rasters of responses to “null” stimulus from a RGC (middle), a fitted linear model (bottom), and a fitted 4-subunit model (top). The firing rate of each model was adjusted by an arbitrary scale factor to approximately match the raster structure in the data.

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