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Related Experiment Video

Updated: Feb 13, 2026

A Method for Growing Bio-memristors from Slime Mold
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Memristor Synapse-A Device-Level Critical Review.

Sridhar Chandrasekaran1, Yao-Feng Chang2, Firman Mangasa Simanjuntak3,4

  • 1Micro and Nano Devices Laboratory, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India.

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|February 12, 2026
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Summary
This summary is machine-generated.

Memristor devices mimic brain plasticity for neuromorphic computing. Optoelectronic synapses using 2D materials offer enhanced control and diverse healthcare applications, advancing artificial intelligence.

Keywords:
LTPSRDPSTDPSTPmemristor synapse

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Memristors are explored for nonvolatile memory and neuromorphic computing due to their brain-like synaptic plasticity.
  • Crossbar arrays enable ultra-high-density integration of memristor synapse devices for large-scale machine learning.
  • Biomimetic properties like short-term plasticity (STP) and long-term plasticity (LTP) are crucial for hardware neural networks.

Purpose of the Study:

  • To statistically analyze neuromorphic computing device publications from 2018 to 2025, focusing on memristive systems.
  • To provide a device-level perspective on biomimetic properties (STP, LTP, STDP, SRDP) in hardware neural networks.
  • To highlight optoelectronic synapses based on 2D materials for mimicking brain plasticity via optical stimuli.

Main Methods:

  • Statistical analysis of scientific publications on neuromorphic computing devices.
  • Device-level examination of biomimetic properties in memristive systems.
  • Review of optoelectronic synapse technologies utilizing 2D materials and optical stimulation.

Main Results:

  • Memristive systems show significant research growth in neuromorphic computing.
  • Optoelectronic synapses based on 2D materials demonstrate effective mimicry of brain plasticity through optical control.
  • Practical applications include MNIST recognition, pattern recognition, and potential healthcare solutions.

Conclusions:

  • Memristor synapses, particularly optoelectronic ones, are promising for advanced AI and neuromorphic systems.
  • Optical stimulation offers novel control mechanisms for memristor-based artificial synapses.
  • Future directions point to significant healthcare applications, including artificial cognition and physiological monitoring.