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  Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

Breuer, D., Timme, M., & Memmesheimer, R. M. (2014). Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling. Physical Review X, 4: 011053. doi:10.1103/PhysRevX.4.011053.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-0F51-D Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0029-0F52-B
Genre: Journal Article

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 Creators:
Breuer, David1, Author              
Timme, Marc1, Author              
Memmesheimer, Raoul Martin1, Author              
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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 Abstract: How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality.

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Language(s): eng - English
 Dates: 2014-03-28
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: eDoc: 699248
DOI: 10.1103/PhysRevX.4.011053
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Title: Physical Review X
Source Genre: Journal
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Pages: - Volume / Issue: 4 Sequence Number: 011053 Start / End Page: - Identifier: -