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  Closed-form treatment of the interactions between neuronal activity and spike-timing-dependent plasticity in networks of linear neurons

Kolodziejski, C., Tetzlaff, C., & Woergoetter, F. (2010). Closed-form treatment of the interactions between neuronal activity and spike-timing-dependent plasticity in networks of linear neurons. Frontiers in Computational Neuroscience, 4: 134. doi:10.3389/fncom.2010.00134.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-120F-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-1210-8
Genre: Journal Article

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 Creators:
Kolodziejski, Christoph1, Author              
Tetzlaff, Christian1, Author              
Woergoetter, Florentin, Author
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1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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 Abstract: Network activity and network connectivity mutually influence each other. Especially for fast processes, like spike-timing-dependent plasticity (STDP), which depends on the interaction of few (two) signals, the question arises how these interactions are continuously altering the behavior and structure of the network. To address this question a time-continuous treatment of plasticity is required. However, this is – even in simple recurrent network structures – currently not possible. Thus, here we develop for a linear differential Hebbian learning system a method by which we can analytically investigate the dynamics and stability of the connections in recurrent networks. We use noisy periodic external input signals, which through the recurrent connections lead to complex actual ongoing inputs and observe that large stable ranges emerge in these networks without boundaries or weight-normalization. Somewhat counter-intuitively, we find that about 40% of these cases are obtained with a long-term potentiation-dominated STDP curve. Noise can reduce stability in some cases, but generally this does not occur. Instead stable domains are often enlarged. This study is a first step toward a better understanding of the ongoing interactions between activity and plasticity in recurrent networks using STDP. The results suggest that stability of (sub-)networks should generically be present also in larger structures.

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Language(s): eng - English
 Dates: 2010-10-27
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 528677
DOI: 10.3389/fncom.2010.00134
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Title: Frontiers in Computational Neuroscience
Source Genre: Journal
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Pages: - Volume / Issue: 4 Sequence Number: 134 Start / End Page: - Identifier: -