The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks

DePasquale, B., Sussillo, D., Abbott, L. F., & Churchland, M. M. (2023). The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks. Neuron, 111(5), 631-649.e10. https://doi.org/10.1016/j.neuron.2022.12.007
Authors:
Brian DePasquale
David Sussillo
L F Abbott
Mark M Churchland
Affiliated Authors:
Brian DePasquale
L F Abbott
Mark M Churchland
Columbia Affiliation:
Subjects:
Neural Networks, Computer (MeSH)
Neurons (MeSH)
Author Keywords:
force learning
artificial neural networks
dimensionality reduction
dynamical systems
factor models
motor system
network training
population dynamics
recurrent neural networks
spiking networks
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Publication Type:
Article
Unique ID:
10.1016/j.neuron.2022.12.007
PMID:
36630961
DOI:
10.1016/j.neuron.2022.12.007
Journal:
Neuron
Publication Date:
Data Source:
PubMed
Source Link:
View PubMed Record

Record Created: