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Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic

Mostafa Shooshtari1, So-Yeon Kim2, Saeideh Pahlavan1

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Halide perovskite memristors can mimic brain learning through spike-timing-dependent plasticity (STDP). This material enables stable, noise-tolerant synaptic learning for advanced neuromorphic computing without external programming.

Keywords:
halide perovskite memristorneuromorphic computingnoise robustnessspike-timing-dependent plasticity (STDP)synaptic plasticitytriplet-STDP

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Area of Science:

  • Neuromorphic engineering
  • Materials science
  • Neuroscience

Background:

  • Neuromorphic engineering merges nanotechnology and neuroscience to replicate brain functions.
  • Spike-timing-dependent plasticity (STDP) is a key learning mechanism in the brain, crucial for synaptic changes.
  • Memristors are emerging devices explored for simulating neural plasticity.

Purpose of the Study:

  • To demonstrate that halide perovskite memristors can simulate biologically plausible STDP dynamics.
  • To develop a physical model for the memristor's switching behavior.
  • To explore the potential of these devices in neuromorphic computing.

Main Methods:

  • Fabrication and characterization of a Cs3Bi2I6Br3 halide perovskite memristor.
  • Development of a dynamic physical model for memristor switching.
  • Application of biologically inspired biphasic voltage pulses to induce STDP.
  • Testing stability and noise tolerance with realistic voltage noise.

Main Results:

  • The halide perovskite memristor successfully simulated STDP, including long-term potentiation (LTP) and long-term depression (LTD).
  • The device exhibited advanced STDP features like triplet-STDP and synaptic memory consolidation.
  • STDP behavior remained stable across 100 trials with biologically realistic voltage noise (<0.03% variation).

Conclusions:

  • Inherent physical dynamics of halide perovskites enable bioinspired learning without external programming.
  • These memristors offer a pathway for scalable, low-power, and noise-tolerant synaptic learning.
  • Findings bridge materials physics with spike-based computation for next-generation neuromorphic systems.