A Morrison

Abigail Morrison

EM Izhikevich

JK Jun

JK Liu

Markus Diesmann

Susanne Kunkel

English

PubMed

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Random wiring limits the development of functional structure in large recurrent neuronal networks

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POSTER PRESENTATION Open AccessRandom wiring limits the development offunctional structure in large recurrent neuronalnetworksSusanne Kunkel1,2*, Markus Diesmann2,3,4, Abigail Morrison1,2From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010San Antonio, TX, USA. 24-30 July 2010Spike-timing dependent plasticity (STDP) has tradition-ally been of great interest to theoreticians, as it seems toprovide an answer to the question of how the brain candevelop functional structure in response to repeated sti-muli. However, despite this high level of interest, con-vincing demonstrations of this capacity in large, initiallyrandom networks have not been forthcoming. Suchdemonstrations as there are typically rely on constrain-ing the problem artificially. Techniques include employ-ing additional pruning mechanisms or STDP rules thatenhance symmetry breaking, simulating networks withlow connectivity that magnify competition betweensynapses, or combinations of the above (see, e.g. [1-3]).Here, we describe a theory for the stimulus-drivendevelopment of feed-forward structures in random net-works. The theory explains why the emergence of suchstructures does not take place in unconstrained systems[4] and enables us to identify candidate biologicallymotivated adaptations to the balanced random networkmodel that might facilitate it. Finally, we investigatethese candidate adaptations in large-scale simulations.AcknowledgmentsPartially supported by the Helmholtz Alliance on Systems Biology, the Next-Generation Supercomputer Project of MEXT, EU Grant 15879 (FACETS), DIPF1.2, and BMBF Grant 01GQ0420 to BCCN Freiburg. Access to HPC providedby JUGENE-Grant JINB33. All network simulations were carried out with NEST(http://www.nest-initiative.org).Author details1Functional Neural Circuits, Faculty of Biology, Albert-Ludwig University ofFreiburg, Germany. 2Bernstein Center Freiburg, Albert-Ludwig University ofFreiburg, Germany. 3RIKEN Brain Science Institute, Wako City, Japan. 4RIKENComputational Science Research Program, Wako City, Japan.Published: 20 July 2010References1. Izhikevich EM: Polychronization: computation with spikes. Neural Comput2006, 18:245-282.2. Jun JK, Jin DZ: Development of neural circuitry for precise temporalsequences through spontaneous activity, axon remodeling, and synapticplasticity. PLoS ONE 2007, 2(1):e723.3. Liu JK, Buonomano DV: Embedding multiple trajectories in simulatedrecurrent neural networks in a self-organizing manner. J Neurosci 2009,29(42):13172-81.4. Morrison A, Aertsen A, Diesmann: Spike-timing-dependent plasticity inbalanced random networks. Neural Comput 2007, 19(6):1437-67.doi:10.1186/1471-2202-11-S1-P108Cite this article as: Kunkel et al.: Random wiring limits the developmentof functional structure in large recurrent neuronal networks. BMCNeuroscience 2010 11(Suppl 1):P108.Submit your next manuscript to BioMed Centraland take full advantage of: • Convenient online submission• Thorough peer review• No space constraints or color figure charges• Immediate publication on acceptance• Inclusion in PubMed, CAS, Scopus and Google Scholar• Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submit* Correspondence: kunkel@bcf.uni-freiburg.de1Functional Neural Circuits, Faculty of Biology, Albert-Ludwig University ofFreiburg, GermanyKunkel et al. BMC Neuroscience 2010, 11(Suppl 1):P108http://www.biomedcentral.com/1471-2202/11/S1/P108© 2010 Kunkel et al; licensee BioMed Central Ltd.

BMC Neuroscience

Diesmann Markus

Kunkel Susanne

Morrison Abigail

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POSTER PRESENTATION Open Access
Random wiring limits the development of
functional structure in large recurrent neuronal
networks
Susanne Kunkel1,2*, Markus Diesmann2,3,4, Abigail Morrison1,2
From Nineteenth Annual Computational Neuroscience Meeting: CNS*2010
San Antonio, TX, USA. 24-30 July 2010
Spike-timing dependent plasticity (STDP) has tradition-
ally been of great interest to theoreticians, as it seems to
provide an answer to the question of how the brain can
develop functional structure in response to repeated sti-
muli. However, despite this high level of interest, con-
vincing demonstrations of this capacity in large, initially
random networks have not been forthcoming. Such
demonstrations as there are typically rely on constrain-
ing the problem artificially. Techniques include employ-
ing additional pruning mechanisms or STDP rules that
enhance symmetry breaking, simulating networks with
low connectivity that magnify competition between
synapses, or combinations of the above (see, e.g. [1-3]).
Here, we describe a theory for the stimulus-driven
development of feed-forward structures in random net-
works. The theory explains why the emergence of such
structures does not take place in unconstrained systems
[4] and enables us to identify candidate biologically
motivated adaptations to the balanced random network
model that might facilitate it. Finally, we investigate
these candidate adaptations in large-scale simulations.
Acknowledgments
Partially supported by the Helmholtz Alliance on Systems Biology, the Next-
Generation Supercomputer Project of MEXT, EU Grant 15879 (FACETS), DIP
F1.2, and BMBF Grant 01GQ0420 to BCCN Freiburg. Access to HPC provided
by JUGENE-Grant JINB33. All network simulations were carried out with NEST
(http://www.nest-initiative.org).
Author details
1Functional Neural Circuits, Faculty of Biology, Albert-Ludwig University of
Freiburg, Germany. 2Bernstein Center Freiburg, Albert-Ludwig University of
Freiburg, Germany. 3RIKEN Brain Science Institute, Wako City, Japan. 4RIKEN
Computational Science Research Program, Wako City, Japan.
Published: 20 July 2010
References
1. Izhikevich EM: Polychronization: computation with spikes. Neural Comput
2006, 18:245-282.
2. Jun JK, Jin DZ: Development of neural circuitry for precise temporal
sequences through spontaneous activity, axon remodeling, and synaptic
plasticity. PLoS ONE 2007, 2(1):e723.
3. Liu JK, Buonomano DV: Embedding multiple trajectories in simulated
recurrent neural networks in a self-organizing manner. J Neurosci 2009,
29(42):13172-81.
4. Morrison A, Aertsen A, Diesmann: Spike-timing-dependent plasticity in
balanced random networks. Neural Comput 2007, 19(6):1437-67.
doi:10.1186/1471-2202-11-S1-P108
Cite this article as: Kunkel et al.: Random wiring limits the development
of functional structure in large recurrent neuronal networks. BMC
Neuroscience 2010 11(Suppl 1):P108.
Submit your next manuscript to BioMed Central
and take full advantage of: 
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at 
www.biomedcentral.com/submit
* Correspondence: kunkel@bcf.uni-freiburg.de
1Functional Neural Circuits, Faculty of Biology, Albert-Ludwig University of
Freiburg, Germany
Kunkel et al. BMC Neuroscience 2010, 11(Suppl 1):P108
http://www.biomedcentral.com/1471-2202/11/S1/P108
© 2010 Kunkel et al; licensee BioMed Central Ltd.


Buonomano DV: Embedding multiple trajectories in simulated recurrent neural networks in a self-organizing manner.

Diesmann: Spike-timing-dependent plasticity in balanced random networks.

DZ: Development of neural circuitry for precise temporal sequences through spontaneous activity, axon remodeling, and synaptic plasticity. PLoS ONE

Izhikevich EM: Polychronization: computation with spikes.

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Random wiring limits the development of functional structure in large recurrent neuronal networks

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