Correlated Firing Improves Stimulus Discrimination in a Retinal Model

• Published in: Neural computation Vol. 16; no. 11; pp. 2261 - 2291
• Main Authors: Kenyon, Garrett T., Theiler, James, George, John S., Travis, Bryan J., Marshak, David W.
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.11.2004; MIT Press Journals, The
• 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; Bayes estimation; Spike; Neural computation; Stimulus; Neuronal discharge; Firing rate; Neural network; Information loss; Fano factor; Ganglion neuron; Synchronous oscillation; Feedback; Summation; Threshold
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/0899766041941916

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.