Paper Info
Title
Chimera States in Neuro-Inspired Area-Efficient Asynchronous Cellular Automata Networks
Abstract
Synchronization transition in neuromorphic networks has attracted much attention recently as a fundamental property of biological neural networks, which relies on network connectivity along with different synaptic features. In this work, an area-optimized FPGA implementation of an Asynchronous Cellular Automata Neuron model that exhibits discrete-state neuron dynamics is introduced. The proposed neuron model is capable of reproducing various neuromorphic oscillations observed in biological neurons using less hardware resources than previous implementations. We investigate synchronization transitions with a focus on the emergence of chimera states in a ring-based network consisting of hardware-based neurons with electrical synaptic coupling. In particular, we study the effects on the network’s phase synchronization through changing two control parameters: the coupling range and the coupling strength. We indicate that via proper configuration of the coupling parameters, we influence the synchronization transition and reveal chimera states which have been associated with neurological disorders.
Year
DOI
Venue
2022
10.1109/TCSI.2022.3187376
IEEE Transactions on Circuits and Systems I: Regular Papers
Keywords
DocType
Volume
Asynchronous cellular automaton,FPGA,neuron model,discrete-state dynamics,neuromorphic hardware,chimera states,synchronization transition
Journal
69
Issue
ISSN
Citations 
10
1549-8328
0
PageRank 
References 
Authors
0.34
15
8
Name
Order
Citations
PageRank
Karolos-Alexandros Tsakalos100.34
Paraskevi Dragkola200.34
Rafailia-Eleni Karamani301.35
Michail-Antisthenis Tsompanas400.34
Astero Provata500.34
Panagiotis Dimitrakis600.34
andrew adamatzky71180163.89
Sirakoulis Georgios Ch.830660.41