Proton-gated organic thin-film transistors for leaky integrate-and-fire convolutional spiking neural networks

Artificial spiking neurons , integral to the functionality of spiking neural networks , are designed to mimic the information transmission via discrete spikes in biological nervous systems . Traditional approaches that necessitate the charging of capacitors and the inclusion of discharge circuits for neuron membrane potential integration and leakage, present challenges in terms of cost and space efficiency. To overcome the challenges, this work proposes a hardware leaky integrate-and-fire neuron based on organic thin-film transistors. Under the electric field, the ion dynamics in the gate electrolyte can mimic the processes of membrane potential integration, leakage, and reset in spiking neurons. The convolutional spiking neural networks composed of such organic spiking neurons achieves excellent recognition rates (∼97.26 %) on the MNIST dataset. This indicates that the organic spiking neuron has enormous potential in next-generation non-von Neumann neuromorphic computing . • Utilizing ion-gated organic thin-film transistors (OTFTs) to create leaky integrate-and-fire (LIF) neurons, by hardware-based experimental realization. • Elimination of extra capacitors and transistors traditionally required for simulating membrane potential dynamics and reset mechanisms in LIF neurons, leading to more compact and scalable neuromorphic systems. • Integration of the proposed OTFT-based LIF neurons into a CSNN with a high recognition rate of approximately 97.26 % on the MNIST dataset, validating their potential for next-generation non-von Neumann neuromorphic computing.