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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Mott memristor based stochastic neurons for probabilistic computing.

Aabid Amin Fida¹, Sparsh Mittal¹, Farooq Ahmad Khanday²

¹Electronics and Communication Engineering, Indian Institute of Technology, Roorkee, Uttrakhand, India.

|April 9, 2024

Summary

This summary is machine-generated.

This study introduces a novel stochastic neuron using memristor technology for energy-efficient neuromorphic computing. The developed spiking neural network demonstrates effective probabilistic learning and inference capabilities.

Keywords:

insulator to metal transition nanoscale spiking neural networks stochastic leaky integrate and fire threshold memristor

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Area of Science:

Neuromorphic Engineering
Materials Science
Computational Neuroscience

Background:

Probabilistic spiking in biological systems enhances learning and Bayesian inference.
Stochasticity in nanoscale devices offers potential benefits for neuromorphic systems.

Purpose of the Study:

To develop a stochastic leaky integrate and fire (LIF) neuron utilizing Mott memristor dynamics.
To demonstrate the neuron's capability for biological neural dynamics and probabilistic computation.
To integrate the neuron into advanced neural network architectures for learning and inference tasks.

Main Methods:

Fabrication of a stochastic LIF neuron incorporating a Mott memristor.
Integration of the neuron into a population-coded spiking neural network and a spiking restricted Boltzmann machine (sRBM).
Evaluation of the sRBM's accuracy for probabilistic learning and inference.

Main Results:

The developed LIF neuron exhibits biological neural dynamics.
The integrated sRBM achieved a high accuracy of 87.13%, comparable to software implementations.
The design eliminates the need for external noise sources, unlike CMOS-based probabilistic neurons.

Conclusions:

The Mott memristor-based stochastic LIF neuron enables energy-efficient and compact neuromorphic systems.
The proposed neuron effectively implements probabilistic learning and inference in spiking neural networks.
This approach offers a promising pathway for advanced, low-power neuromorphic computing applications.