Format

Send to

Choose Destination
Int J Neural Syst. 2016 Nov;26(7):1650030. doi: 10.1142/S0129065716500301. Epub 2016 Apr 7.

A Computational Framework for Realistic Retina Modeling.

Author information

1
1 Department of Computer Architecture and Technology, CITIC-UGR, University of Granada, Spain.

Abstract

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

KEYWORDS:

Computational retina modeling; adaptation to the mean light intensity; contrast adaptation; large-scale retina model; low-pass temporal filter; object motion sensitive cells; retina simulator; short-term plasticity; single-cell retina model; single-compartment model; space-variant Gaussian filter; spiking neural networks; static nonlinearity; visual adaptation

PMID:
27354192
DOI:
10.1142/S0129065716500301
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Atypon Icon for ModelDB
Loading ...
Support Center