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Updated: Jul 12, 2026

A Method for Growing Bio-memristors from Slime Mold

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Published on: November 2, 2017

Modeling multiple classical conditioning mechanisms in a Memristor-Based learning circuit.

Yueqi Song¹, Suo Gao¹, Herbert Ho-Ching Iu²

¹School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 9, 2026

Summary

This summary is machine-generated.

This study introduces a novel analog neural circuit using threshold memristors to model complex associative learning, successfully replicating multiple classical conditioning phenomena in a brain-inspired system.

Keywords:

Associative learning Classical conditioning Multimodal stimuli Threshold memristor

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Area of Science:

Neuroscience
Cognitive Science
Materials Science

Background:

Brain-inspired computing aims to replicate biological learning mechanisms.
Memristors offer promising analog synaptic plasticity for neural circuits.
Existing models often focus on single associative learning processes.

Purpose of the Study:

To propose and validate an analog neural circuit for modeling multiple complex associative learning mechanisms.
To demonstrate the capability of threshold memristors in emulating classical conditioning phenomena.
To develop a unified memristive framework for higher-order associative learning.

Main Methods:

Design of a rate-based analog neural circuit incorporating multimodal sensory stimuli (gustatory, visual, auditory).
Utilizing threshold memristors for dynamical synaptic regulation across neural pathways.
Validation through PSPICE simulations to demonstrate synaptic plasticity and associative learning processes.

Main Results:

Successful emulation of classical conditioning processes: acquisition, second-order conditioning, overshadowing, blocking, extinction, and reacquisition.
Demonstration of synaptic competition underlying overshadowing and blocking.
Validation of second-order conditioning pathway using memristor synaptic plasticity.

Conclusions:

The proposed unified memristive framework effectively models multiple associative learning processes.
This work extends the application of memristors in brain-inspired computing and cognitive hardware.
The rate-based analog circuit provides a biologically inspired approach to higher-order associative learning.