This title appears in the Scientific Report :
2014
Please use the identifier:
http://dx.doi.org/10.1371/journal.pcbi.1003428 in citations.
Please use the identifier: http://hdl.handle.net/2128/5803 in citations.
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
The Correlation Structure of Local Neuronal Networks Intrinsically Results from Recurrent Dynamics
Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of infor...
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Personal Name(s): | Helias, Moritz |
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Tetzlaff, Tom / Diesmann, Markus | |
Contributing Institute: |
Computational and Systems Neuroscience; INM-6 Computational and Systems Neuroscience; IAS-6 |
Published in: | PLoS Computational Biology, 10 (2014) 1, S. e1003428 |
Imprint: |
San Francisco, Calif.
Public Library of Science
2014
|
DOI: |
10.1371/journal.pcbi.1003428 |
PubMed ID: |
24453955 |
Document Type: |
Journal Article |
Research Program: |
Theory, modelling and simulation The Next-Generation Integrated Simulation of Living Matter Supercomputing and Modelling for the Human Brain Brain-inspired multiscale computation in neuromorphic hybrid systems Signalling Pathways and Mechanisms in the Nervous System |
Link: |
Get full text OpenAccess |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/5803 in citations.
Correlated neuronal activity is a natural consequence of network connectivity and shared inputs to pairs of neurons, but the task-dependent modulation of correlations in relation to behavior also hints at a functional role. Correlations influence the gain of postsynaptic neurons, the amount of information encoded in the population activity and decoded by readout neurons, and synaptic plasticity. Further, it affects the power and spatial reach of extracellular signals like the local-field potential. A theory of correlated neuronal activity accounting for recurrent connectivity as well as fluctuating external sources is currently lacking. In particular, it is unclear how the recently found mechanism of active decorrelation by negative feedback on the population level affects the network response to externally applied correlated stimuli. Here, we present such an extension of the theory of correlations in stochastic binary networks. We show that (1) for homogeneous external input, the structure of correlations is mainly determined by the local recurrent connectivity, (2) homogeneous external inputs provide an additive, unspecific contribution to the correlations, (3) inhibitory feedback effectively decorrelates neuronal activity, even if neurons receive identical external inputs, and (4) identical synaptic input statistics to excitatory and to inhibitory cells increases intrinsically generated fluctuations and pairwise correlations. We further demonstrate how the accuracy of mean-field predictions can be improved by self-consistently including correlations. As a byproduct, we show that the cancellation of correlations between the summed inputs to pairs of neurons does not originate from the fast tracking of external input, but from the suppression of fluctuations on the population level by the local network. This suppression is a necessary constraint, but not sufficient to determine the structure of correlations; specifically, the structure observed at finite network size differs from the prediction based on perfect tracking, even though perfect tracking implies suppression of population fluctuations. |