Topological acoustic synapse for high-dimensional neuromorphic computing.

Overview

Authors: Jinli Chen^1,2, Akinsanmi S Ige^1,2, Keith Runge^1,2, Pierre A Deymier^1,2, Xiaodong Yan^1,2,3,4

ORCID iDs: Jinli Chen, Akinsanmi S Ige, Keith Runge, Xiaodong Yan

Department of Materials Science and Engineering, University of Arizona, Tucson, AZ 85721, USA
New Frontiers of Sound Science and Technology Center, University of Arizona, Tucson, AZ 85721, USA
Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721, USA
James C. Wyant College of Optical Sciences, University of Arizona, Tucson, AZ 85721, USA

Institutions: University of Arizona (United States)

Journal: Science advances, volume 12, issue 24, article eaec6633

Dates: received 28 September 2025; accepted 4 May 2026; published online 12 June 2026; in print June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1126/sciadv.aec6633 · PMID 42284405 · PMCID PMC13262634 · OpenAlex W7164542725

Open access: gold, a free copy (OpenAlex)

Status: data only

Categories: cellular / molecular (subfield)

Methods: Spectral & time-frequency, Machine learning

Topic: Ferroelectric and Negative Capacitance Devices (Electrical and Electronic Engineering, Engineering), according to OpenAlex

Funding: National Science Foundation (2441746, 2242925); Air Force Office of Scientific Research (FA9550-26-1-0326)

Citations: not cited yet (Europe PMC); 66 references in the paper

Abstract

The human brain performs complex, high-dimensional (HD) computations, such as causal reasoning, counterfactual thinking, and abstraction, with ~1011 neurons while consuming ~20 watts of power. Neuromorphic computing seeks similar efficiency, but current devices face bottlenecks in bandwidth, energy, wiring, footprint, and reliability that limit scalability. Here, we introduce the topological acoustic synapse (TAS), an acoustic-wave neuromorphic device that circumvents these limits by mapping information in multivariate state spaces. A single TAS generates and manipulates numerous computing channels that operate independently and in parallel. The TAS leverages nonlinear interactions to emulate biorealistic neuromorphic functionalities, including reconfigurable synaptic plasticity, neuromodulation, and hybrid analog-digital control. In classification tasks, a TAS handles multiple inputs simultaneously and generates various outputs, converging 20% faster while using 60% fewer parameters and at least an order of magnitude less power than state-of-the-art electrical devices. This work establishes the first acoustic synapse with parallel HD computing capabilities, presenting a scalable paradigm for neuromorphic hardware with high computational density.

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.

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

doi:10.7910/dvn/2cdqvi, at the source; found in “Data Availability Statement”

Data, code, and materials availability

Data and code are available at Harvard Dataverse (https://doi.org/10.7910/DVN/2CDQVI). All data and code needed to evaluate and reproduce the results in the paper are presented in the paper and online data and/or the Supplementary Materials. This study did not generate new materials.

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

Versions

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Version 2, 28 September 2026

Funding: added National Science Foundation: 2441746, 2242925; Air Force Office of Scientific Research: FA9550-26-1-0326

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 40 references.

Cite

This paper

Chen, J., Ige, A. S., Runge, K., Deymier, P. A., & Yan, X. (2026). Topological acoustic synapse for high-dimensional neuromorphic computing. Science advances, 12(24), eaec6633. https://doi.org/10.1126/sciadv.aec6633

BibTeX

@article{chen2026topological, author = {Chen, Jinli and Ige, Akinsanmi S and Runge, Keith and Deymier, Pierre A and Yan, Xiaodong}, title = {{Topological acoustic synapse for high-dimensional neuromorphic computing}}, journal = {Science advances}, year = {2026}, month = jun, volume = {12}, number = {24}, pages = {eaec6633}, publisher = {American Association for the Advancement of Science}, issn = {2375-2548}, doi = {10.1126/sciadv.aec6633}, url = {https://doi.org/10.1126/sciadv.aec6633}, pmid = {42284405}, pmcid = {PMC13262634} }

RIS

TY - JOUR AU - Chen, Jinli AU - Ige, Akinsanmi S AU - Runge, Keith AU - Deymier, Pierre A AU - Yan, Xiaodong TI - Topological acoustic synapse for high-dimensional neuromorphic computing T2 - Science advances J2 - Sci Adv PY - 2026 DA - 2026/06/12 VL - 12 IS - 24 SP - eaec6633 SN - 2375-2548 PB - American Association for the Advancement of Science DO - 10.1126/sciadv.aec6633 UR - https://doi.org/10.1126/sciadv.aec6633 LA - en ER -

CSL-JSON

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