May 2004
Volume 45, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2004
Neural encoding of the rod photoreceptor response
Author Affiliations & Notes
  • G.A. Silva
    Departments of Bioengineering and Ophthalmology, University of California, San Diego, La Jolla, CA
  • Footnotes
    Commercial Relationships  G.A. Silva, None.
  • Footnotes
    Support  UCSD/Whitaker Foundation Development Award
Investigative Ophthalmology & Visual Science May 2004, Vol.45, 3644. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      G.A. Silva; Neural encoding of the rod photoreceptor response . Invest. Ophthalmol. Vis. Sci. 2004;45(13):3644.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Abstract: : Purpose: Understanding the neural code is a central requirement for advanced technologies that aim to interface and communicate with the CNS. Most of what is known about the neural code is within the context of spiking CNS neurons. Very little is known about the statistical properties of the neural code of photoreceptors. In the present work a computational model is developed that describes the encoding of stepped light stimuli by rod photoreceptors. Methods: The conceptual foundation of the present work is a theoretical model of the dynamics of light adaptation (1) which describes the dynamic light adapting rod response in terms of summating elemental photic signals: uj(i) = ωj εDA(i,Ib) + (1–ωj) εLA(i,Ib), where ωj is a weighting parameter that determines the peak amplitude and shape of the elemental response uj(i) at times j, and εDA(i,Ib) and εLA(i,Ib) are the derived single photon responses in the dark and maximally light–adapted conditions (see (1) and (2)). From this, a statistical model based on Bayes' rule was developed, adapted from similar statistical encoding/decoding models characterizing the neural code of CNS spiking neurons (3). Implementations of the present model were done in Matlab (Mathworks, Natick, MA). Results: The dynamic rod response to the onset of stepped backgrounds of light (e.g. intensities on the order of Ib = 1.2 and 0.4 sc cd m–2) was determined as it evolved towards steady state without a priori knowledge of the response, in effect representing the neural encoding of the input stimuli. Inputs S(t) were assumed to occur with probability P[S(t)], while intermediate light adapting single photon responses uj(maxi,inti) (i.e. the single photon rod response as it evolves towards steady state) were assumed to be taken from the probability distribution P[uj(maxj,intj)]. The kinetics of uj(maxi,inti) are defined in terms of the responses' peak amplitude (maxj) and integration time (intj), the full time course of the responses being computed using an optimization algorithm. Conclusions: The neural encoding of rod photoreceptor responses to stepped background light inputs were modeled in terms of dynamic single photon responses who's evolution towards steady state are defined within the context of Bayesian statistics. The structure of the present model may provide a framework for modeling and understanding how rods encode and represent dynamic light stimuli, thereby complimenting our understanding of neural encoding/decoding by spiking neurons. (1) Silva and Pepperberg (in press) IEEE Trans. Biomed. Eng., January 2004. (2) Silva et. al. (2001) J. Physiol. 543:203. (3) Rieke et. al.(1997) Spikes: Characterizing the neural code. MIT Press.

Keywords: computational modeling • photoreceptors • signal transduction 
×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×