Cited by 353Advanced Memory and Neural ComputingConducting polymers and applicationsNeural dynamics and brain function

Neuromorphic device architectures with global connectivity through electrolyte gating.

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Gkoupidenis, Paschalis, Koutsouras, Dimitrios A., Malliaras, George G.·2017·Nature Communications

Abstract

Information processing in the brain takes place in a network of neurons that are connected with each other by an immense number of synapses. At the same time, neurons are immersed in a common electrochemical environment, and global parameters such as concentrations of various hormones regulate the overall network function. This computational paradigm of global regulation, also known as homeoplasticity, has important implications in the overall behaviour of large neural ensembles and is barely addressed in neuromorphic device architectures. Here, we demonstrate the global control of an array of organic devices based on poly(3,4ethylenedioxythiophene):poly(styrene sulf) that are immersed in an electrolyte, a behaviour that resembles homeoplasticity phenomena of the neural environment. We use this effect to produce behaviour that is reminiscent of the coupling between local activity and global oscillations in the biological neural networks. We further show that the electrolyte establishes complex connections between individual devices, and leverage these connections to implement coincidence detection. These results demonstrate that electrolyte gating offers significant advantages for the realization of networks of neuromorphic devices of higher complexity and with minimal hardwired connectivity.

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  • There is currently a consensus that neurons and synapses constitute the fundamental electrochemical processing units of the human brain.

    “However, there is currently a consensus that neurons and synapses constitute the fundamental electrochemical processing units of the human brain”

    From Human Brain/Cloud Interface (2019) · cites this paper as Gkoupidenis et al., 2017

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  1. Kandel E. R., Schwartz J. H. & Jessell T. M. Principles of Neural Science McGraw-Hill, Health Professions Division (2000).

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Authors
Gkoupidenis, P., Koutsouras, D. A., & Malliaras, G. G.
Corresponding author
Malliaras, George G.
Venue
Nature Communications
Published
2017-05-17
Volume
8
Issue
1
Pages
15448-15448
ISSN
2041-1723 (ISSN-L)
OpenAlex
W2614342775
Cited by
353
References
48
Citations by year
2026: 28 · 2025: 34 · 2024: 32 · 2023: 44 · 2022: 49 · 2021: 60 · 2020: 41 · 2019: 35 · 2018: 28 · 2017: 2
FWCI
12.05
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Cambridge University Engineering Department Publications Database (submittedVersion)
  • MPG.PuRe (Max Planck Society) (submittedVersion)
  • Apollo (University of Cambridge) (submittedVersion, cc-by)
  • Apollo (cc-by)
Funders
University of Southampton
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
  • Physical Sciences › Materials Science › Polymers and Plastics › Conducting polymers and applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Keywords
neuromorphic hardware, electrolyte gating, organic field-effect transistors, PEDOT:PSS, global connectivity, coincidence detection, neural oscillations, organic electronics, neuromorphic networks
MeSH terms
Brain, Electrolytes, Models, Neurological, Nerve Net, Neurons, Polystyrenes, Synapses, Thiophenes, Neural Networks, Computer, Microtechnology

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