Abstract: Neuromorphic computing is an exciting and rapidly growing field that aims to create computing systems that can replicate the complex and dynamic behavior of the human brain. Organic electrochemical transistors (OECTs) have emerged as a promising tool for developing such systems due to their unique bioelectronic properties. In this paper, we present a novel approach for signal classification using an OECT array, which exhibits multifunctional bioelectronic functionality similar to neurons and synapses li nked through a global medium. Our approach takes advantage of the intrinsic device variabilities of OECTs to create a reservoir network with variable neuron-time constants and synaptic strengths. We demonstrate the effectiveness of our approach by classi fying surface-electromyogram (sEMG) signals into three hand gesture categories. The OECT array performs efficient signal acquisition by feeding signals through multiple gates and measuring the response to a group of OECTs with a global liquid medium. We compare the performance of our approach with and without projecting the input on OECTs and observe a significant increase in classification accuracy from 40% to 68%. We also examined how the classification performance is affected by different selection s trategies and numbers of OECTs used. Finally, we developed a spiking neural network-based simulation that mimics the OECTs array and found that OECT-based classification is comparable to the spiking neural network-based approach. Our work paves the way f or the next generation of low-power, real-time, and intelligent biomedical sensing systems.

Abstract: Neural networks are powerful tools for solving complex problems, but finding the right network topology for a given task remains an open question. Biology uses neurogenesis and structural plasticity to solve this problem. Advanced neural network algorith ms are mostly relying on synaptic plasticity and learning. The main limitation in reconciling these two approaches is the lack of a viable hardware solution that could reproduce the bottom-up development of biological neural networks. Here, we show how t he dendritic growth of PEDOT:PSS-based fibers through AC electropolymerization can implement structural plasticity during network development. We find that this strategy follows Hebbian principles and is able to define topologies that leverage better com puting performances with sparse synaptic connectivity for solving non-trivial tasks. This approach is validated in software simulation, and offers up to 61% better network sparsity on classification and 50% in signal reconstruction tasks.