Single organic electrochemical neuron capable of anticoincidence detection
Padinhare Cholakkal Harikesh, Dace Gao, Hanyan Wu, Chi‐Yuan Yang, Deyu Tu, Simone Fabiano
Abstract
Emulating complex neural computations like solving linearly inseparable tasks within single artificial neurons has remained an elusive goal in neuromorphic engineering. Here, we report a dendritic organic electrochemical neuron (d-OECN) capable of achieving anticoincidence detection by classifying the exclusive-OR (XOR) problem-a quintessential linearly inseparable task-within an individual neuron. Inspired by human cortical neurons that perform XOR through dendritic calcium spikes, the d-OECN leverages ion-tunable antiambipolarity in mixed ionic-electronic conducting polymers to mimic voltage-gated dendritic calcium dynamics. By integrating this dendritic component with a tunable spiking circuit representing the soma, the d-OECN achieves XOR classification through its inherent nonlinear activation profile, with decision boundaries that are both ionically and electrically tunable. Moreover, we demonstrate the d-OECN's ability to perform edge detection using XOR in a tactile sensing system, showcasing its potential for event-based sensing and processing. The d-OECNs, replicating key aspects of biological intelligence, pave the way for next-generation bioelectronics and robotics requiring complex neural computation.