Hodge-Kodaira decomposition of evolving neural networks

Keiji Miura, Takaaki Aoki

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Although it is very important to scrutinize recurrent structures of neural networks for elucidating brain functions, conventional methods often have difficulty in characterizing global loops within a network systematically. Here we applied the Hodge-Kodaira decomposition, a topological method, to an evolving neural network model in order to characterize its loop structure. By controlling a learning rule parametrically, we found that a model with an STDP-rule, which tends to form paths coincident with causal firing orders, had the most loops. Furthermore, by counting the number of global loops in the network, we detected the inhomogeneity inside the chaotic region, which is usually considered intractable.

Original languageEnglish
Pages (from-to)20-24
Number of pages5
JournalNeural Networks
Publication statusPublished - 2015 Feb 1


  • Chaos
  • Co-evolving network dynamics
  • Hodge-Kodaira decomposition
  • Phase oscillators
  • Spike-timing-dependent plasticity
  • Topology

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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