Evidence map›Paper›PMID 39554511›Full record

ReviewPNAS nexus2024

Neuromorphic neuromodulation: Towards the next generation of closed-loop neurostimulation.

Luis Fernando Herbozo Contreras, Nhan Duy Truong, Jason K Eshraghian, Zhangyu Xu, Zhaojing Huang, Thomas Vincenzo Bersani-Veroni, Isabelle Aguilar, Wing Hang Leung, Armin Nikpour, Omid Kavehei

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Explainability in AI-enabled medical neurotechnology: a scoping review.Journal of neuroengineering and rehabilitation · 2026
    Article
  4. Materials and System Design for Self-Decision Bioelectronic Systems.Advanced materials (Deerfield Beach, Fla.) · 2026
    Review
  5. Article
  6. Review
  7. Review
  8. Can neuromorphic computing help reduce AI's high energy cost?Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  9. Review
  10. Review
  11. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Luis Fernando Herbozo ContrerasSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0009-0001-8458-9486
Nhan Duy TruongSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0003-4350-8026
Jason K EshraghianDepartment of Electrical and Computer Engineering, University of California, Santa Cruz 95064, USA.ORCID https://orcid.org/0000-0002-5832-4054
Zhangyu XuSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0002-1536-2499
Zhaojing HuangSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0009-0004-2796-6734
Thomas Vincenzo Bersani-VeroniSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
Isabelle AguilarSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0009-0005-4072-7700
Wing Hang LeungSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.
Armin NikpourCentral Clinical School, The University of Sydney, Sydney, NSW 2006, Australia.
Omid KaveheiSchool of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia.ORCID https://orcid.org/0000-0002-2753-5553

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neuromodulation techniques have emerged as promising approaches for treating a wide range of neurological disorders, precisely delivering electrical stimulation to modulate abnormal neuronal activity. While leveraging the unique capabilities of AI holds immense potential for responsive neurostimulation, it appears as an extremely challenging proposition where real-time (low-latency) processing, low-power consumption, and heat constraints are limiting factors. The use of sophisticated AI-driven models for personalized neurostimulation depends on the back-telemetry of data to external systems (e.g. cloud-based medical mesosystems and ecosystems). While this can be a solution, integrating continuous learning within implantable neuromodulation devices for several applications, such as seizure prediction in epilepsy, is an open question. We believe neuromorphic architectures hold an outstanding potential to open new avenues for sophisticated on-chip analysis of neural signals and AI-driven personalized treatments. With more than three orders of magnitude reduction in the total data required for data processing and feature extraction, the high power- and memory-efficiency of neuromorphic computing to hardware-firmware co-design can be considered as

Indexed as

bio-inspired algorithmsneural networksneuromodulationneuromorphicon-chip learning

Identifiers

PMID39554511
PMCPMC11565243

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.