Statistical physics of neural systems with nonadditive dendritic coupling

Research output: Contribution to journalResearch articleContributedpeer-review


  • David Breuer - , Max Planck Institute for Dynamics and Self-Organization, Max Planck Institute of Molecular Plant Physiology (Author)
  • Marc Timme - , Max Planck Institute for Dynamics and Self-Organization, University of Göttingen, Bernstein Center for Computational Neuroscience Göttingen (Author)
  • Raoul Martin Memmesheimer - , Radboud University Nijmegen (Author)


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.


Original languageEnglish
Article number011053
JournalPhysical Review X
Issue number1
Publication statusPublished - 28 Mar 2014
Externally publishedYes

External IDs

ORCID /0000-0002-5956-3137/work/142242474


ASJC Scopus subject areas


  • Biological physics, Complex systems, Statistical physics

Library keywords