Connectionists: Branch-specific plasticity enables self-organization of nonlinear computation in single neurons

[Legenstein Robert]

The paper

*Branch-specific plasticity enables self-organization of nonlinear 
computation in single neurons*

by

Robert Legenstein and Wolfgang Maass

has been published in /The Journal of Neuroscience/ and is available 
online at

http://www.jneurosci.org/content/31/30/10787.full.pdf+html

In this article, we investigate how experimentally observed plasticity
mechanisms, such as depolarization-dependent STDP and branch-strength
potentiation, could be integrated to self-organize nonlinear neural
computations with dendritic spikes.


/Abstract:/

It has been conjectured that nonlinear processing in dendritic
branches endows individual neurons with the capability to perform
complex computational operations that are needed in order to solve for
example the binding problem. However, it is not clear how single
neurons could acquire such functionality in a self-organized manner,
since most theoretical studies of synaptic plasticity and learning
concentrate on neuron models without nonlinear dendritic
properties. In the meantime, a complex picture of information
processing with dendritic spikes and a variety of plasticity
mechanisms in single neurons has emerged from experiments. In
particular, new experimental data on dendritic branch strength
potentiation in rat hippocampus have not yet been incorporated into
such models. In this article, we investigate how experimentally
observed plasticity mechanisms, such as depolarization-dependent STDP
and branch-strength potentiation could be integrated to self-organize
nonlinear neural computations with dendritic spikes. We provide a
mathematical proof that in a simplified setup these plasticity
mechanisms induce a competition between dendritic branches, a novel
concept in the analysis of single neuron adaptivity. We show via
computer simulations that such dendritic competition enables a single
neuron to become member of several neuronal ensembles, and to acquire
nonlinear computational capabilities, such as for example the
capability to bind multiple input features. Hence our results suggest
that nonlinear neural computation may self-organize in single neurons
through the interaction of local synaptic and dendritic plasticity
mechanisms.

-- 
----------------------------------------

Dr. Robert Legenstein
Institut für Grundlagen der Informationsverarbeitung
Technische Universität Graz
Inffeldgasse 16b/I, 8010 Graz, Austria

++43/316/873-5824
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