Original Research Article

A model for cortical rewiring following deafferentation and focal stroke

1
Bernstein Center for Computational Neuroscience, University of Göttingen, Germany
2
Department of Integrative Neurophysiology, Neuroscience Campus Amsterdam, Vrije Universiteit, The Netherlands

It is still unclear to what extent structural plasticity in terms of synaptic rewiring is the cause for cortical remapping after a lesion. Recent two-photon laser imaging studies demonstrate that synaptic rewiring is persistent in the adult brain and is dramatically increased following brain lesions or after a loss of sensory input (cortical deafferentation). We use a recurrent neural network model to study the time course of synaptic rewiring following a peripheral lesion. For this, we represent axonal and dendritic elements of cortical neurons to model synapse formation, pruning and synaptic rewiring. Neurons increase and decrease the number of axonal and dendritic elements in an activity-dependent fashion in order to maintain their activity in a homeostatic equilibrium. In this study we demonstrate that synaptic rewiring contributes to neuronal homeostasis during normal development as well as following lesions. We show that networks in homeostasis, which can therefore be considered as adult networks, are much less able to compensate for a loss of input. Interestingly, we found that paused stimulation of the networks are much more effective promoting reorganization than continuous stimulation. This can be explained as neurons quickly adapt to this stimulation whereas pauses prevents a saturation of the positive stimulation effect. These findings may suggest strategies for improving therapies in neurologic rehabilitation.

Keywords: lesion-induced plasticity, neuronal network model, homeostasis, structural plasticity, cortical remapping, synaptogenesis, axonal sprouting, neurological rehabilitation

Citation: Butz M, van Ooyen A and Wörgötter F (2009) A model for cortical rewiring following deafferentation and focal stroke. Front. Comput. Neurosci. 3:10. doi:10.3389/neuro.10.010.2009

Received: 22 August 2008; Paper pending published: 02 December 2008; Accepted: 16 July 2009; Published online: 04 August 2009.

Edited by: 
Klaus R. Pawelzik, University of Bremen, Germany

Reviewed by: 
Maoz Shamir, Boston University, USA
Markus Diesmann, RIKEN Brain Science Institute, Japan; RIKEN Computational Science Research Program, Japan

Copyright: © 2009 Butz, van Ooyen and Wörgötter. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.

*Correspondence: Prof. W�rg�tter, Bernstein Center for Computational Neuroscience, University of Goettingen, Bunsenstr. 10, 37073 G�ttingen, Germany, worgott@bccn-goettingen.de

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