Evidence map›Paper›PMID 31796727›Full record

ArticleNature communications2019

Optimal solid state neurons.

Kamal Abu-Hassan, Joseph D Taylor, Paul G Morris, Elisa Donati, Zuner A Bortolotto, Giacomo Indiveri, Julian F R Paton, Alain Nogaret

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. A nanofluidic oscillating neuron.Nature communications · 2025
    Article
  2. Article
  3. Rapid, interpretable data-driven models of neural dynamics using recurrent mechanistic models.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  4. Article
  5. Article
  6. An organic electrochemical neuron for a neuromorphic perception system.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Review
  14. Multiplexed Complementary Signal Transmission for a Self-Regulating Artificial Nervous System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  15. Emerging memristive neurons for neuromorphic computing and sensing.Science and technology of advanced materials · 2023
    Review
  16. Article
  17. Article
  18. Article
  19. Beyond LIF Neurons on Neuromorphic Hardware.Frontiers in neuroscience · 2022
    Article
  20. Article
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

8 authors.

Kamal Abu-HassanDepartment of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, UK.ORCID http://orcid.org/0000-0002-1982-1306
Joseph D TaylorDepartment of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, UK.ORCID http://orcid.org/0000-0002-9882-9727
Paul G MorrisDepartment of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, UK.ORCID http://orcid.org/0000-0002-4554-2132
Elisa DonatiInstitute of Neuroinformatics, University of Zürich and ETH Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland.
Zuner A BortolottoSchool of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, BS8 1TD, UK.ORCID http://orcid.org/0000-0002-5513-8061
Giacomo IndiveriInstitute of Neuroinformatics, University of Zürich and ETH Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland.ORCID http://orcid.org/0000-0002-7109-1689
Julian F R PatonSchool of Physiology, Pharmacology and Neuroscience, University of Bristol, Bristol, BS8 1TD, UK.
Alain NogaretDepartment of Physics, University of Bath, Claverton Down, Bath, BA2 7AY, UK. A.R.Nogaret@bath.ac.uk.ORCID http://orcid.org/0000-0003-0164-5815

Funding

British Heart Foundation PG/11/47/28980
6 · The paper itself

Abstract

Bioelectronic medicine is driving the need for neuromorphic microcircuits that integrate raw nervous stimuli and respond identically to biological neurons. However, designing such circuits remains a challenge. Here we estimate the parameters of highly nonlinear conductance models and derive the ab initio equations of intracellular currents and membrane voltages embodied in analog solid-state electronics. By configuring individual ion channels of solid-state neurons with parameters estimated from large-scale assimilation of electrophysiological recordings, we successfully transfer the complete dynamics of hippocampal and respiratory neurons in silico. The solid-state neurons are found to respond nearly identically to biological neurons under stimulation by a wide range of current injection protocols. The optimization of nonlinear models demonstrates a powerful method for programming analog electronic circuits. This approach offers a route for repairing diseased biocircuits and emulating their function with biomedical implants that can adapt to biofeedback.

Indexed as

AnimalsBrain StemHippocampusIon Channel GatingIon ChannelsMaleModels, NeurologicalNeuronsPyramidal CellsRats, WistarRespirationIon Channels

Identifiers

PMID31796727
PMCPMC6890780

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.