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Biological receptor-inspired flexible artificial synapse based on ionic dynamics.

Qifeng Lu1, Fuqin Sun1, Lin Liu2

  • 1i-Lab, Key Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics (SINANO), Chinese Academy of Sciences (CAS), 398 Ruoshui Road, 215123 Suzhou, PR China.

Microsystems & Nanoengineering
|September 27, 2021
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Summary
This summary is machine-generated.

Researchers developed a bioinspired memristor using reduced graphene oxide and chitosan. This artificial synaptic device mimics brain functions like learning and forgetting, offering a solution for neuromorphic computing challenges.

Keywords:
Nanoscale devicesOther nanotechnology

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • The von Neumann architecture faces bottlenecks in traditional computing.
  • Neuromorphic computing aims to overcome these limitations by mimicking the brain.
  • Memristors are key components for building artificial neural networks.

Purpose of the Study:

  • To develop a novel bioinspired memristive device for artificial synaptic emulation.
  • To investigate the potential of reduced graphene oxide (rGO) and chitosan in creating a biorealistic synaptic device.
  • To demonstrate learning and forgetting behaviors in the memristor, mimicking human brain functions.

Main Methods:

  • Fabrication of a double-layer memristive device using rGO nanosheets coated with chitosan.
  • Investigation of the device's working mechanism, inspired by G-protein-linked receptors.
  • Analysis of memristive behavior resulting from proton interactions with rGO functional groups and defects.
  • Characterization of channel current due to proton hopping and trap limitations.

Main Results:

  • The novel device exhibits smooth changes in response current based on historical applied voltages, enabling biorealistic synaptic emulation.
  • Memristive behavior is attributed to proton interactions within the chitosan-rGO channel.
  • The device successfully demonstrates the transition from short-term to long-term potentiation.
  • Learning and forgetting behaviors, analogous to human brain functions, were observed.

Conclusions:

  • The bioinspired memristor-type artificial synaptic device shows significant potential for advancing neuromorphic networks.
  • The use of rGO and chitosan offers a promising pathway for creating efficient and brain-like computing components.
  • This research contributes to overcoming the limitations of traditional computing architectures through bio-mimicry.