Pavlov's dog associative learning demonstrated on synaptic-like organic transistors
This work addresses hardware implementation of neural networks for low-power associative learning, but it is incremental as it builds on existing memristive device concepts.
The authors tackled the problem of implementing associative learning in hardware by demonstrating a neural network using nanoparticle organic memory field effect transistors (NOMFETs) as synapses, achieving low-power write operations and validating the scheme with reproducible electronic circuits.
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.