Publication date: 22nd July 2026
Neuromorphic systems often emulate neuronal firing by combining memory elements with dedicated reset mechanisms [1] or strongly nonlinear switching devices [2]. In this work, we show that repetitive spike generation can emerge from a considerably simpler framework [3]. We investigate a threshold-responsive memristive circuit composed of a passive RC network and a dynamic conductance element described by a single internal variable [4]. When driven by periodic voltage pulses, the system displays stable sequences of current spikes despite the absence of reset operations, oscillatory feedback loops, or negative differential resistance.
Through numerical and analytical analysis, we find that spike generation is governed by the competition between memory evolution and charge accumulation processes. The firing regime is not determined by static device properties alone, but by the synchronization of several characteristic timescales associated with relaxation, charging, leakage and external stimulation. Within a specific dynamical window, these processes self-organize into repetitive cycles of energy storage and rapid release, producing well-defined spiking activity.
A notable feature of the mechanism is the appearance of an effective separation between fast and slow dynamics, even though the memristive element contains only a single intrinsic memory timescale. This emergent behavior results from the nonlinear interaction between the internal state and its voltage-dependent operating point. We derive simple criteria that predict the onset and stability of the firing regime and identify the conditions leading to spike suppression or irregular activity.
These results suggest that neuronal-like firing can be achieved in a broader class of adaptive electronic materials than previously assumed. By emphasizing dynamical operation rather than static switching characteristics, this work provides new guidelines for the design of compact neuromorphic hardware based on memristive technologies.
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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