Akam, TE;
Kullmann, DM;
(2012)
Efficient "communication through coherence" requires oscillations structured to minimize interference between signals.
PLoS Comput Biol
, 8
(11)
, Article e1002760. 10.1371/journal.pcbi.1002760.
Preview |
PDF
1373128.pdf Download (2MB) |
Abstract
The 'communication through coherence' (CTC) hypothesis proposes that selective communication among neural networks is achieved by coherence between firing rate oscillation in a sending region and gain modulation in a receiving region. Although this hypothesis has stimulated extensive work, it remains unclear whether the mechanism can in principle allow reliable and selective information transfer. Here we use a simple mathematical model to investigate how accurately coherent gain modulation can filter a population-coded target signal from task-irrelevant distracting inputs. We show that selective communication can indeed be achieved, although the structure of oscillatory activity in the target and distracting networks must satisfy certain previously unrecognized constraints. Firstly, the target input must be differentiated from distractors by the amplitude, phase or frequency of its oscillatory modulation. When distracting inputs oscillate incoherently in the same frequency band as the target, communication accuracy is severely degraded because of varying overlap between the firing rate oscillations of distracting inputs and the gain modulation in the receiving region. Secondly, the oscillatory modulation of the target input must be strong in order to achieve a high signal-to-noise ratio relative to stochastic spiking of individual neurons. Thus, whilst providing a quantitative demonstration of the power of coherent oscillatory gain modulation to flexibly control information flow, our results identify constraints imposed by the need to avoid interference between signals, and reveal a likely organizing principle for the structure of neural oscillations in the brain.
Type: | Article |
---|---|
Title: | Efficient "communication through coherence" requires oscillations structured to minimize interference between signals. |
Location: | United States |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1371/journal.pcbi.1002760 |
Publisher version: | http://dx.doi.org/10.1371/journal.pcbi.1002760 |
Language: | English |
Additional information: | © 2012 Akam, Kullmann. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by the Wellcome Trust and European Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
Keywords: | Animals, Brain, Cell Communication, Computational Biology, Computer Simulation, Mammals, Models, Neurological, Nerve Net, Neurons |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy |
URI: | https://discovery.ucl.ac.uk/id/eprint/1373128 |
Archive Staff Only
View Item |