Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs is a research paper published in eLife (2023). On theSindex it has a DataRank of 0. It has been cited 12 times.
Scored on demand from live citation data
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
National Institute of Neurological Disorders and Stroke
Grant: R01NS062200
National Institute of Neurological Disorders and Stroke
Grant: R01NS125863
DOE Office of Science User Facility
Grant: DE-AC02-06CH11357
National Science Foundation Next Generation Networks
Grant: NSF DBI 2015317
Canadian Institutes of Health Research
Grant: unidentified
unidentified
National Institutes of Health
Grant: 1R01NS125863-01A1
Supercomputer-based Models of Motoneurons for Estimating Their Synaptic Inputs in Humans
National Science Foundation
Grant: 2015317
NeuroNex: Communication, Coordination, and Control in Neuromechanical Systems (C3NS)
National Institutes of Health
Grant: 5R01NS062200-03
Computer Models of Normal and Abnormal Discharge Patterns in Human Motoneurons
Fields of Study
MeSH Terms
Keywords