Correlated Firing Improves Stimulus Discrimination in a Retinal Model

Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information...

Full description

Saved in:
Bibliographic Details
Published in:Neural computation 2004-11, Vol.16 (11), p.2261-2291
Main Authors: Kenyon, Garrett T., Theiler, James, George, John S., Travis, Bryan J., Marshak, David W.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial intelligence
Bayes Theorem
Biofeedback, Psychology - physiology
Biological and medical sciences
Cognition & reasoning
Computer science
control theory
systems
Electrophysiology
Exact sciences and technology
Eyes & eyesight
Fundamental and applied biological sciences. Psychology
General aspects
General aspects. Models. Methods
Learning and adaptive systems
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Neurological
Neurons
Poisson Distribution
Retina - cytology
Retina - physiology
Retinal Ganglion Cells - physiology
Synapses - physiology
Vertebrates: nervous system and sense organs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.
ISSN:0899-7667
1530-888X
DOI:10.1162/0899766041941916