Quantitative Analysis of Dynamical Complexity in Cultured Neuronal Network Models for Reservoir Computing Applications

Satoshi Moriya, Hideaki Yamamoto, Ayumi Hirano-Iwata, Shigeru Kubota, Shigeo Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reservoir computing is a machine learning paradigm that was proposed as a model of cortical information processing in the brain. It processes information using the spatiotemporal dynamics of a large-scale recurrent neural network and is expected to improve power efficiency and speed in neuromorphic computing systems. Previous theoretical investigation has shown that brain networks exhibit an intermediate state of full coherence and random firing, which is suitable for reservoir computing. However, how reservoir performance is influenced by connectivity, especially which revealed in recent connectomics analysis of brain networks, remains unclear. Here, we constructed modular networks of integrate-and-fire neurons and investigated the effect of modular structure and excitatory-inhibitory neuron ratio on network dynamics. The dynamics were evaluated based on the following three measures: synchronous bursting frequency, mean correlation, and functional complexity. We found that in a purely excitatory network, the complexity was independent of the modularity of the network. On the other hand, networks with inhibitory neurons exhibited complex network activity when the modularity was high. Our findings reveal a fundamental aspect of reservoir performance in brain networks, contributing to the design of bio-inspired reservoir computing systems.

Original languageEnglish
Title of host publication2019 International Joint Conference on Neural Networks, IJCNN 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728119854
DOIs
Publication statusPublished - 2019 Jul
Event2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
Duration: 2019 Jul 142019 Jul 19

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2019-July

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period19/7/1419/7/19

Keywords

  • complexity
  • modular networks
  • reservoir computing

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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