Organic Oscillator-Based Spiking Neuron with Tunable Excitability
Nikita Prudnikov a, Hans Kleemann a
a Dresden Integrated Center for Applied Physics and Photonic Materials (IAPP), Germany
Proceedings of MATSUS Fall 2025 Conference (MATSUSFall25)
D3 Brain-Inspired Computation: Memristors, Oscillators, and Networks - #NeuroComp
València, Spain, 2025 October 20th - 24th
Organizers: Juan Bisquert, Beatriz Noheda and Martin F. Sarott
Oral, Nikita Prudnikov, presentation 161
Publication date: 21st July 2025

Neuromorphic electronics aims to replicate the functionality of biological neurons, offering a promising route to energy-efficient data processing. One of the key application areas is bioelectronic interfacing, where neuromorphic systems can enable efficient, real-time analysis of biological signals at the edge. Organic electrochemical transistors (OECTs) are biocompatible, operate at low voltages, and support flexible, low-cost fabrication, making them ideal for bioelectronic interfaces and edge-processing in biosensing applications. Additionally, OECTs are particularly well-suited for such applications due to their unique ability to couple ionic and electronic transport for integration of electronic circuits with ion-driven biological systems.

Despite these advantages, implementing artificial neuron circuits that emulate the functional characteristics of their biological counterparts using organic materials remains a major challenge. In this work, we present an organic artificial neuron circuit based on a multivibrator – a well-known oscillatory circuit. The proposed neuron exhibits spiking activity with the ability of tuning intrinsic neuron excitability, which is considered to be involved in the learning process alongside synaptic plasticity. Apart from providing input-dependent spiking dynamics, the neuron demonstrates short-term memory based on its previous spiking activity. This study advances the development of accessible, neuromorphic hardware by introducing a new class of organic artificial neurons.

The authors acknowledge the project ArNeBOT funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)—536022519.

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