Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings

Network. 2013;24(1):27-51. doi: 10.3109/0954898X.2012.740140. Epub 2012 Nov 29.


It has recently become possible to identify cone photoreceptors in primate retina from multi-electrode recordings of ganglion cell spiking driven by visual stimuli of sufficiently high spatial resolution. In this paper we present a statistical approach to the problem of identifying the number, locations, and color types of the cones observed in this type of experiment. We develop an adaptive Markov Chain Monte Carlo (MCMC) method that explores the space of cone configurations, using a Linear-Nonlinear-Poisson (LNP) encoding model of ganglion cell spiking output, while analytically integrating out the functional weights between cones and ganglion cells. This method provides information about our posterior certainty about the inferred cone properties, and additionally leads to improvements in both the speed and quality of the inferred cone maps, compared to earlier "greedy" computational approaches.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Adaptation, Physiological
  • Algorithms
  • Animals
  • Computer Simulation
  • Electrophysiological Phenomena
  • Likelihood Functions
  • Linear Models
  • Macaca fascicularis
  • Macaca mulatta
  • Microelectrodes
  • Monte Carlo Method*
  • Nonlinear Dynamics
  • Photic Stimulation
  • Poisson Distribution
  • Retinal Cone Photoreceptor Cells / physiology*
  • Retinal Ganglion Cells / physiology*