Evidence map›Paper›PMID 42591065›Full record

ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Neuromorphic Devices and Computing for Sensing, Memory, and Control.

Zhengguang Zhu, Nicholas Schaffer, Xiao Yang

Abstract readReview
In one paragraph

Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

3 authors.

Zhengguang ZhuDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0009-0004-2315-806X
Nicholas SchafferDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0009-0001-7334-5451
Xiao YangDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA.ORCID https://orcid.org/0000-0003-1504-4949

Funding

Johns Hopkins University Startup FundMaryland Stem Cell Research Fund Launch Program
6 · The paper itself

Abstract

Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function of the nervous system. For neuromorphic devices, we first describe strategies that mimic subcellular neural functions. We then highlight how device architecture recapitulates biological topology from subcellular components to brain networks. We next summarize how neuromorphic devices emulate sensory and sensorimotor (sensory-modulation) neural circuits. For neuromorphic computing, we review recent advances in artificial and biological neuromorphic computing, including spiking neural networks, bioinspired learning algorithms, and applications. We also discuss emerging biohybrid intelligence systems leveraging two- and three-dimensional biological networks as computational units. Finally, we outline key challenges, potential milestones, and future directions.

Indexed as

artificial neural networkbioelectronicsbiological neural networkbrain–computer interfaceneuromorphic engineeringneuromorphic hardwareorganoid

Identifiers

PMID42591065
PMCPMC13470304

What OpenQuestion holds

Textmetadata
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.