A Buehlmann

C Börgers

Christoph Kirst

D Battaglia

Demian Battaglia

M Besserve

Marc Timme

P Fries

T Womelsdorf

Y Kawamura

Y Kuramoto

English

PubMed

Christoph ChristophKirst

Springer

Local control of non-local information flow in oscillatory neuronal networks

Crossref

BMC Neuroscience

Timme Marc

Kirst Christoph

Battaglia Demian

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ORAL PRESENTATION Open Access
Local control of non-local information flow
in oscillatory neuronal networks
Christoph Kirst1,2*, Marc Timme1,2, Demian Battaglia1,2
From Twentieth Annual Computational Neuroscience Meeting: CNS*2011
Stockholm, Sweden. 23-28 July 2011
Control of information flow between neurons or groups of
neurons is essential in a functional brain, e.g. for context
and brain state dependent processing. In line with recent
experimental and theoretical studies [1-5] we show that
phase relations between synchronized oscillatory local cir-
cuits or brain areas may dynamically create information
channels and induce changes in the effective connectivity.
Reducing neuronal oscillatory dynamics to a phase -
amplitude description [6,7], we show how alternative
phase shifts between different neurons or groups of neu-
rons result in different effective connectivities. In particu-
lar, to quantify the information flow, we analytically
calculate the time delayed mutual information and trans-
fer entropy between oscillators in a phase locked state.
We further present a theoretical framework to predict
phase lag patterns within and between groups of oscilla-
tors in hierarchical networks. Combining both results we
derive the information flow between the oscillators as a
function of structural and dynamical network parameter.
We use our results to reveal how effective connectivity
is controlled by the underlying physical connectivity and
the intrinsic single oscillation frequencies. Interestingly,
we find that local changes in the strength of a single
link can remotely control the effective connectivity
between two different physically unchanged oscillators.
Similarly, local inputs modulating the intrinsic frequen-
cies can dynamically and remotely change the informa-
tion flow between distal nodes.
We link our results to biophysically more realistic net-
works of spiking neurons. In a clustered network of groups
of type I neurons exhibiting gamma oscillations emanating
from a PING mechanism [8], we numerically show that
local changes of the connectivity or the inputs strengths
within a cluster can non-locally control the phase relations
and the information flow between distant clusters.
Conclusion
Our findings reveal that local changes, e.g. in the physical
strength of a local link or in the local frequency due to
variation in the local inputs, can remotely and dynami-
cally control the direction of non-local global information
flow between distal nodes/clusters in a network. This
might provide an efficient local mechanism to control
global information processing in neuronal systems and to
account for contextual and attentional modulation.
Author details
1Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37073,
Germany. 2Bernstein Center for Computational Neuroscience, Göttingen,
37073, Germany.
Published: 18 July 2011
References
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doi:10.1186/1471-2202-12-S1-O15
Cite this article as: Kirst et al.: Local control of non-local information
flow in oscillatory neuronal networks. BMC Neuroscience 2011 12(Suppl 1):
O15.
* Correspondence: ckirst@nld.ds.mpg.de
1Max Planck Institute for Dynamics and Self-Organization, Göttingen, 37073,
Germany
Full list of author information is available at the end of the article
Kirst et al. BMC Neuroscience 2011, 12(Suppl 1):O15
http://www.biomedcentral.com/1471-2202/12/S1/O15
© 2011 Kirst et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.


Chemical Oscillations, Waves and Turbulence.

Collective Phase Sensitivity. Phys Rev Lett

Deco G: Optimal Information Transfer in the Cortex through Synchronization. PLoS Comput Biol

Effects of Noisy Drive on Rhythms in Networks of Excitatory and Inhibitory Neurons. Neural Comp

F: Dynamic transitions in the effective connectivity of interacting cortical areas. FENS

Fries P: Modulation of Neuronal Interactions Through Neuronal Synchronization. Science

High frequency phase-spike synchronization of extracellular signals modulates causal interactions in monkey primary visual cortex. Neuroscience Meeting Planner Society for Neuroscience;

Neuronal Gamma-Band Synchronization as a Fundamental Process in Cortical Computation. Ann Rev Neurosc

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Local control of non-local information flow in oscillatory neuronal networks

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