Long-range temporal correlations in scale-free neuromorphic networks

Author:

Shirai Shota1ORCID,Acharya Susant Kumar1ORCID,Bose Saurabh Kumar1ORCID,Mallinson Joshua Brian1ORCID,Galli Edoardo1ORCID,Pike Matthew D.2,Arnold Matthew D.3ORCID,Brown Simon Anthony1ORCID

Affiliation:

1. The MacDiarmid Institute for Advanced Materials and Nanotechnology, School of Physical and Chemical Sciences, Te Kura Matū, University of Canterbury, Christchurch, New Zealand

2. Electrical and Electronics Engineering, University of Canterbury, Christchurch, New Zealand

3. School of Mathematical and Physical Sciences, University of Technology Sydney, Australia

Abstract

Biological neuronal networks are the computing engines of the mammalian brain. These networks exhibit structural characteristics such as hierarchical architectures, small-world attributes, and scale-free topologies, providing the basis for the emergence of rich temporal characteristics such as scale-free dynamics and long-range temporal correlations. Devices that have both the topological and the temporal features of a neuronal network would be a significant step toward constructing a neuromorphic system that can emulate the computational ability and energy efficiency of the human brain. Here we use numerical simulations to show that percolating networks of nanoparticles exhibit structural properties that are reminiscent of biological neuronal networks, and then show experimentally that stimulation of percolating networks by an external voltage stimulus produces temporal dynamics that are self-similar, follow power-law scaling, and exhibit long-range temporal correlations. These results are expected to have important implications for the development of neuromorphic devices, especially for those based on the concept of reservoir computing.

Funder

MacDiarmid Institute for Advanced Materials and Nanotechnology

Marsden Fund

Ministry of Business, Innovation and Employment

Publisher

MIT Press - Journals

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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