Pavlov's dog associative learning demonstrated on synaptic-like organic transistors

TL;DR

This paper demonstrates Pavlovian associative learning using organic transistors that mimic synapses, showing how memristive devices can perform low-power learning and short-term associations in neural networks.

Contribution

It introduces a novel implementation of associative learning using NOMFET devices, bridging organic electronics and neural network models.

Findings

Successful demonstration of associative learning with NOMFET devices

Low power consumption during learning operations

Reproducible results despite device complexity

Abstract

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low power write operations for the learning and implement short-term association using temporal coding and spike timing dependent plasticity based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.