From bioelectrochemical dendrites to memristors: Electrical tools for understanding synaptic activity in neuromorphic devices
Enrique H. Balaguera a, Antoine Baron b, Sébastien Pecqueur b, Juan Bisquert c
a Escuela Superior de Ciencias Experimentales y Tecnología, Universidad Rey Juan Carlos, C/ Tulipán, s/n, 28933 Móstoles, Madrid, Spain
b IEMN, UMR 8520 Université de Lille, CNRS, Université Polytechnique Hauts-de-France, 59000 Lille, France
c Instituto de Tecnología Química (Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València), 46022 València, Spain
Proceedings of Neuronics Conference 2026 (Neuronics26)
Seoul, Korea, Republic of, 2026 September 8th - 10th
Organizers: Valeria Bragaglia and Seyoung Kim
Oral, Enrique H. Balaguera, presentation 005
Publication date: 24th July 2026

Understanding the electrical mechanisms underlying artificial synaptic plasticity is essential for neuromorphic engineering. This work presents a unified electrical perspective connecting the simple bioelectrochemical dendrites and the greater complexity of memristors as complementary platforms for studying memory effects in artificial brain-inspired synapses. First, we show how conducting polymer dendrites exhibit fractional-order transient responses arising from coupled ionic and electrochemical processes, reproducing key features of synaptic plasticity such as history-dependent dynamics and synaptic learning behavior during morphogenesis [1,2]. Similar electrical signatures are observed in halide perovskite memristors, where ion migration and charge accumulation govern conductance modulation and short-/long-term potentiation [3,4]. By combining Impedance Spectroscopy and transient analysis, we explain the electrical tools for quantifying the physics-based memory effects across biology and neuromorphic engineering. This electrical approach bridges bioelectrochemistry and neuromorphic hardware, offering new insights into the physical origins of synaptic functionality while providing practical guidance for the design and interpretation of next-generation brain-inspired electronic systems.

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