Evidence map›Paper›PMID 39412386›Full record

ArticleeLife2024

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs.

Matthieu K Chardon, Y Curtis Wang, Marta Garcia, Emre Besler, J Andrew Beauchamp, Michael D'Mello, Randall K Powers, Charles J Heckman

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

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  13. Relationships among motor unit discharge parameters used to estimate synaptic inputs to motoneurons.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024
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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.

Matthieu K ChardonDepartment of Neuroscience, Northwestern University, Chicago, United States.ORCID https://orcid.org/0000-0001-5723-3217
Y Curtis WangDepartment of Electrical and Computer Engineering, California State University, Los Angeles, Los Angeles, United States.
Marta GarciaArgonne Leadership Computing Facility, Argonne National Laboratory, Lemont, United States.
Emre BeslerDepartment of Electrical Engineering, Northwestern University, Evanston, United States.
J Andrew BeauchampDepartment of Biomedical Engineering, Northwestern University, Chicago, United States.
Michael D'MelloIntel Corporation, Santa Clara, United States.
Randall K PowersDepartment of Physiology and Biophysics, University of Washington, Seattle, United States.
Charles J HeckmanDepartment of Neuroscience, Northwestern University, Chicago, United States.ORCID https://orcid.org/0000-0002-1324-7278

Funding

Supercomputer-based Models of Motoneurons for Estimating Their Synaptic Inputs in HumansR01NS125863 · NINDS · NORTHWESTERN UNIVERSITY AT CHICAGO · PI Charles Heckman · 2022 to 2026
$3.3M
Computer Models of Normal and Abnormal Discharge Patterns in Human MotoneuronsR01NS062200 · NINDS · UNIVERSITY OF WASHINGTON · PI POWERS, RANDALL K · 2009 to 2013
$2.4M
DOE Office of Science User Facility DE-AC02-06CH11357National Science Foundation Next Generation Networks NSF DBI 2015317NINDS NIH HHS R01 NS062200NINDS NIH HHS R01NS062200NINDS NIH HHS R01 NS125863NINDS NIH HHS R01NS125863
6 · The paper itself

Abstract

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75-90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

Indexed as

Models, NeurologicalMotor NeuronsSynapsesAction PotentialsAnimalsComputer SimulationNeuronscomputational biologycomputational neurosciencehigh performance computingmotoneuronmotor controlmotor unitneurosciencenonesuper computersystems biology

Identifiers

PMID39412386
PMCPMC11483124

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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 OpenQuestion graph.