Publication date: 24th July 2026
The ability to generate spike trains is a defining feature of neuronal systems and a key requirement for neuromorphic hardware. In most neuron models and hardware realizations, repetitive firing is achieved through dedicated reset operations [1] or through nonlinear mechanisms that periodically drive the system back to a resting state [2]. This has led to the widespread assumption that some form of reset process is an essential ingredient for integrate-and-fire dynamics.
In this work, we challenge this view by demonstrating that regular spike generation can arise in a simple memristive circuit whose dynamics remain continuous at all times [3]. The proposed architecture combines a passive RC network with a threshold-responsive memory element [4] and is driven by periodic excitation pulses. Despite the absence of reset rules, oscillatory instabilities, or negative differential resistance, the system exhibits robust sequences of well-defined spikes.
The origin of this behavior is traced to a dynamical mismatch between the evolution of the circuit voltage and the adaptation of the internal memory state. Under suitable operating conditions, this mismatch creates recurrent episodes of charge accumulation and rapid release, producing a firing pattern that closely resembles the functionality of integrate-and-fire neurons.
Our results indicate that the transition from quiescent to spiking behavior cannot be predicted solely from static device characteristics. Instead, the response emerges from the interaction between device kinetics and external stimulation. This observation suggests that materials previously regarded as unsuitable for neuromorphic neurons may become viable when operated within the appropriate dynamical regime.
This work was funded by the European Research Council (ERC) via Horizon Europe Advanced Grant, grant agreement nº 101097688 ("PeroSpiker"). Additional institutional support from the Severo Ochoa Excellence Program CEX2021-001230-S, funded by MCIN/AEI/10.13039/501100011033, and MENEU project (20250002) funded by the Universitat Politècnica de València is gratefully acknowledged.
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