Interacting linear and nonlinear characteristics produce population coding asymmetries between ON and OFF cells in the retina Academic Article uri icon

Overview

MeSH Major

  • Models, Neurological
  • Neural Pathways
  • Retina
  • Retinal Ganglion Cells

abstract

  • The early visual system is a model for understanding the roles of cell populations in parallel processing. Cells in this system can be classified according to their responsiveness to different stimuli; a prominent example is the division between cells that respond to stimuli of opposite contrasts (ON vs OFF cells). These two cell classes display many asymmetries in their physiological characteristics (including temporal characteristics, spatial characteristics, and nonlinear characteristics) that, individually, are known to have important roles in population coding. Here we describe a novel distinction between the information that ON and OFF ganglion cell populations carry in mouse--that OFF cells are able to signal motion information about both light and dark objects, while ON cells have a selective deficit at signaling the motion of dark objects. We found that none of the previously reported asymmetries in physiological characteristics could account for this distinction. We therefore analyzed its basis via a recently developed linear-nonlinear-Poisson model that faithfully captures input/output relationships for a broad range of stimuli (Bomash et al., 2013). While the coding differences between ON and OFF cell populations could not be ascribed to the linear or nonlinear components of the model individually, they had a simple explanation in the way that these components interact. Sensory transformations in other systems can likewise be described by these models, and thus our findings suggest that similar interactions between component properties may help account for the roles of cell classes in population coding more generally.

publication date

  • September 16, 2013

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC3771031

Digital Object Identifier (DOI)

  • 10.1523/JNEUROSCI.1004-13.2013

PubMed ID

  • 24027295

Additional Document Info

start page

  • 14958

end page

  • 73

volume

  • 33

number

  • 37