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

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Adaptive memristor-based LIF neuron circuit for energy efficient SNN crossbar array.

M S Deepthi¹, H R Shashidhara¹, S B Rudraswamy²

¹ECE Department, The National Institute of Engineering, Visvesvaraya Technological University, Mysuru, KA India.

Cognitive Neurodynamics

|July 6, 2026

Summary

This summary is machine-generated.

This study introduces an adaptive leaky-integrate-and-fire neuron using memristors for efficient spiking neural networks (SNNs). The novel circuit design enhances neuromorphic computing with bio-realistic spikes and reduced energy consumption.

Keywords:

Adaptive leaky-integrate-fire neuron Adaptive spike neural networks Neuromorphic computing Volatile memristor

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Last Updated: Jul 7, 2026

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Area of Science:

Neuromorphic Engineering
Computational Neuroscience
Materials Science

Background:

Spiking neural networks (SNNs) mimic biological brains for efficient computing.
Frequency adaptation in neurons significantly boosts SNN performance.
Memristors offer promising material for neuromorphic hardware.

Purpose of the Study:

To implement an adaptive leaky-integrate-and-fire (LIF) neuron using memristors.
To design and simulate a novel SNN crossbar circuit with adaptive capabilities.
To demonstrate bio-realistic spike generation and energy efficiency in neuromorphic applications.

Main Methods:

Utilized volatile and non-volatile memristors for adaptive LIF neuron implementation.
Designed 2x2 and 5x5 SNN crossbar circuits.
Employed Cadence Virtuoso 180 nm simulation environment to analyze firing dynamics.

Main Results:

Demonstrated control of adaptive neuron response via input inter-pulse interval and circuit parameters.
Achieved bio-realistic spike generation with reduced energy per spike.
Eliminated the need for additional neuron reset circuitry, enhancing scalability.

Conclusions:

The adaptive SNN circuit offers improved performance and efficiency for neuromorphic architectures.
The memristor-based adaptive LIF neuron shows potential for future low-power, brain-inspired computing.
This work paves the way for efficient neuromorphic applications through advanced SNN circuit design.