Simulating Self-Discharge of Organic Neuromorphic Devices in Spiking Neural Networks
Daniel Felder a b, Felix Schmitz a b, John Linkhorst b, Matthias Wessling a b
a DWI - Leibniz Institute for Interactive Materials, Forckenbeckstraße, 50, Aachen, Germany
b AVT.CVT - Chair of Chemical Process Engineering, RWTH Aachen University
Proceedings of Neuronics Conference (Neuronics)
València, Spain, 2024 February 21st - 23rd
Organizers: Sabina Spiga and Juan Bisquert
Oral, Felix Schmitz, presentation 034
Publication date: 18th December 2023

With the advent of neural network tools like ChatGPT and DALL·E the energy demand for neural network servers continually rises to facilitate the neural network needs of people across the globe. Neuromorphic devices can drastically lower the energy demand of such neural network algorithms. Organic neuromorphic devices based on the bio-compatible conductive polymer PEDOT:PSS provide the additional capability to interface with living tissue and microfluidic systems. However, organic neuromorphic devices suffer from self-discharge caused by parasitic electrochemical reactions. These redox shuttle reactions are modeled by combining two-phase charge transport models with electrochemical self-discharge models. The successful implementation of these models allows for the accurate and extensive simulation of single-device discharge behavior and its influencing factors.[1]

Implementation of the single-device simulation into a simulated single-layer network reveals a 100 % accurate prediction over ten hours, even under significant weight drift. For multi-layer networks, however, the prediction performance degrades significantly after 20 minutes due to the weight deterioration caused by the self-discharge. Periodic reminder pulses are necessary to retain network performance. [2]

The necessity for complex compensation mechanisms of self-discharge can be eliminated in spiking neural networks (SNN). Inspired by biology, spiking neural networks implement local and always-on learning. When built with organic neuromorphic devices, these networks constantly relearn and reinforce forgotten states. Implementing an accurate surrogate model for the organic neuromorphic devices into a Brian 2 simulation enables the performance analysis of a spiking neural network on organic neuromorphic hardware. The surrogate model was derived from the high-resolution charge transport model of the previous simulations. 28 x 28 MNIST images were evaluated with a two-layer network. The network achieved competitive recognition results for its size. Networks using devices with self-discharge even achieve higher prediction accuracy than ideal devices without self-discharge. Online learning with idle rates of up to 90% can keep the network performance steady and close to the initial accuracy.[3]

These results reinforce the potential of organic neuromorphic devices for use in brain-inspired computing. Possible avenues for device integration include targeted drug delivery, bio-interfacing and sensing applications, and close integration with multi-electrode arrays.

Matthias Wessling acknowledges DFG funding through the Gottfried Wilhelm Leibniz Award 2019 (WE 4678/12-1).

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