Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications.

Overview

1. Instituto de Tecnología Química (ITQ), Consejo Superior de Investigaciones Científicas-Universitat Politècnica de València, 46022 València, Spain

Abstract

Self-sustained oscillators are emerging as key physical elements for neuromorphic electronics, providing a hardware route to emulate the spiking dynamics of biological neurons. As conventional computing architectures struggle with power dissipation and parallel processing limitations, oscillatory devices offer a means to reproduce the brain’s remarkable efficiency–performing adaptive and nonlinear tasks with minimal energy consumption. This review provides a unified synthesis of the diverse families of self-oscillating systems developed across physics, chemistry, and electronic engineering. We classify oscillators according to their operational mechanisms, distinguishing those driven by negative differential resistance (NDR) instabilities from those sustained by active-feedback amplifiers. Their common behavior is described within a nonlinear dynamical framework that links materials, electronic response, and the emergence of limit cycles in phase space. We discuss how these devices–ranging from electrochemical and memristive oscillators to transistor-based and hybrid architectures–can be modeled, measured, and coupled to form complex networks. Particular attention is given to the experimental identification of active elements and impedance signatures that reveal self-oscillation. By bridging device physics, nonlinear dynamics, and neuromorphic computing, this review outlines a coherent foundation for designing scalable, energy-efficient oscillatory systems that connect the physical principles of chemical and electronic oscillators with the computational logic of the brain.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

• zenodo:17338256, at Zenodo; found in “Data Availability Statement”
• zenodo:17338257, at Zenodo; found in DataCite

Data Availability Statement

The data presented here can be accessed at 10.5281/zenodo.17338256 (https://doi.org/10.5281/zenodo.17338256) (Zenodo) under the license CC BY 4.0 (Creative Commons Attribution 4.0 International).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

• Publisher: n/a → American Chemical Society

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 1 funder, 314 references.

Cite

This paper

Rivera-Sierra, G., Bisquert, J., & Fenollosa, R. (2026). Self-Oscillatory Neuron-like Devices for Unconventional Computing Applications. Chemical reviews, 126(10), 5792-5851. https://doi.org/10.1021/acs.chemrev.5c00878

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1021/acs.jpclett.6c01632

Spiking without Resets: Continuous Integrate-and-Fire Dynamics in Neuronal Circuits.

Journal: The journal of physical chemistry letters

In common: 14 references, 2 authors

[2] doi:10.1021/acs.jpclett.6c01968

Light-Induced Neuron-Like Bursts.

Journal: The journal of physical chemistry letters

In common: 1 reference, 3 authors

[3] doi:10.1038/s41598-026-55218-0

Amorphous metal-oxide semiconductor thin-film neuron, synapse, and neuromorphic system.

Journal: Scientific reports

In common: 4 references

[4] doi: [code]

Limit-Cycle Proliferation Under Parametric Delayed Feedback in a Conductance-Based Neuron: Bifurcation Landscape, Orbit Catalog, and Capacity Analysis

Journal: Entropy (Basel, Switzerland)

In common: 4 references

[5] doi:10.1038/s41467-026-74460-8 [code]

Spike-based alignment learning solves the weight transport problem.

Journal: Nature communications

In common: 4 references

[6] doi:10.1038/s41467-026-73825-3 [code]

Volatile self-selective memristive neuron for millisecond-latency neuromorphic object detection at the edge.

Journal: Nature communications

In common: 3 references

[7] doi:10.1038/s41467-026-72900-z [code]

Resistive memory-based neural differential equation solver for score-based diffusion model.

Journal: Nature communications

In common: 3 references

[8] doi: [code]

Self-Rectifying Integrate-and-Fire Neuron and Collaborative Trim Training Framework for SNN-Based EEG Motor Imagery Classification

Journal: Brain sciences

In common: 3 references

[9] doi:10.1371/journal.pbio.3003735 [code]

A widespread animal communication tempo may resonate with the receiver's brain.

Journal: PLoS biology

In common: 3 references

[10] doi:10.7554/elife.105482 [code]

Principles of gamma synchrony predict figure-ground perception in texture stimuli.

Journal: eLife

In common: 3 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.