Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.

Hoda Fares, Margherita Ronchini, Milad Zamani, Hooman Farkhani, Michela Chiappalone, Emre Neftci, Farshad Moradi

Abstract readReview

In one paragraph

Review in npj biomedical innovations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Hoda FaresBao Group, Department of Chemical Engineering, Stanford University, Stanford, CA, USA. hfares@ece.au.dk.

Margherita RonchiniDepartment of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark.

Milad ZamaniDepartment of Mechanical and Electrical Engineering, University of Southern Denmark, Odense, Denmark.

Hooman FarkhaniDepartment of Mechanical and Electrical Engineering, University of Southern Denmark, Odense, Denmark.

Michela ChiappaloneDepartment of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Genova, Italy.

Emre NeftciPeter Grünberg Institute, Forschungszentrum Jülich GmbH, Jülich, Germany.

Farshad MoradiDepartment of Mechanical and Electrical Engineering, University of Southern Denmark, Odense, Denmark. hfares@stanford.edu.

Funding

European Union's Horizon 2020 research and innovation program No 767092Lundbeck Foundation R402-2022-1413

6 · The paper itself

Abstract

Neurological disorders pose a growing global health burden, motivating advances in neural interfacing and artificial intelligence (AI). This review surveys state-of-the-art approaches for neural recording, stimulation and signal decoding and encoding across brain-computer interfaces, neuroprosthetics, and neuromodulation systems aimed at restoring function and treating neurological disorders. It highlights recent progress in AI, particularly neuromorphic computing and spiking neural networks (SNNs), and introduces Brain-Inspired Brain-Computer Interfaces (BI-BCIs) as a unifying framework for low-power, closed-loop, and miniaturized neuromorphic neurotechnologies.

Identifiers

PMID42625007

DOI10.1038/s44385-026-00074-w

PMCPMC13493818

What Socratic holds

Textmetadata

LicenceCC BY-NC-ND

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 Socratic graph.