Brain-inspired energy efficient technologies for next-generation artificial intelligence.

Hillel J Chiel, Jay S Coggan, Gourav Datta, Jean-Marc Fellous, William R P Nourse, Roger D Quinn, Peter J Thomas

Abstract readReview

In one paragraph

Review in Biological cybernetics, 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.

Hillel J ChielDepartment of Biology, Case Western Reserve University, Cleveland, USA.

Jay S CogganNeuroLinx Research Institute, La Jolla, USA.

Gourav DattaDepartment of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, USA.

Jean-Marc FellousInstitute for Neural Computation, University of California San Diego, La Jolla, USA.

William R P NourseDepartment of Mechanical Engineering, Case Western Reserve University, Cleveland, USA.

Roger D QuinnDepartment of Mechanical Engineering, Case Western Reserve University, Cleveland, USA.

Peter J ThomasDepartment of Biology, Case Western Reserve University, Cleveland, USA. pjthomas@case.edu.

Funding

National Science Foundation 2342866National Science Foundation DBI 2015317National Science Foundation DMS 2052109NIH HHS RO1 NS118606

6 · The paper itself

Abstract

Since the advent of widely accessible AI tools, AI technology has been in high demand by businesses, academic researchers and individuals. Technology companies are building AI infrastructure at a rapid pace, and these facilities consume vast and growing resources, particularly electricity and water, with significant real and projected climate impacts. There is a need for new research initiatives to support long time horizon efforts to develop energy efficient computing capabilities to support the continued growth of AI infrastructure in a sustainable fashion. Such efficiency is required at both the hardware and software levels. Where can industry turn for examples of ultra-low power, energy efficient computing? We argue here that neurobiological principles offer rich and under-exploited sources of inspiration for energy efficient NeuroAI, and that new partnerships between industry and academia should be developed in this direction.

Indexed as

Artificial IntelligenceBrainAnimalsHumansArtificial intelligenceBioFlopEnergy efficiencyNeuroscience

Identifiers

PMID41731107

DOI10.1007/s00422-026-01038-4

PMCPMC12929283

What Socratic holds

Textmetadata

LicenceCC BY

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Registered trials

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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.