Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons is a research paper published in PLoS Computational Biology (2020). On theSindex it has a DataRank of 0. It has been cited 21 times.
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NINDS NIH HHS
Grant: R01 NS099375
NINDS NIH HHS
Grant: R01 NS084844
NIBIB NIH HHS
Grant: R01 EB022872
NINDS NIH HHS
Grant: U01 NS094302
NINDS NIH HHS
Grant: R01 NS104928
National Institutes of Health
Grant: 1R01NS104928-01
Thalamocortical state control of tactile sensing: Mechanisms, Models, and Behavior
National Institutes of Health
Grant: 1R01EB022872-01
Neural mechanisms and behavioral consequences of non-Gaussian likelihoods in sensorimotor learning
National Science Foundation
Grant: 1822677
CRCNS Research Proposal: Randomness and systematicity in neural codes for motor exploration
National Institutes of Health
Grant: 5R01NS099375-02
Spike timing codes for motor control
National Institutes of Health
Grant: 3U01NS094302-03S1
MULTISCALE ANALYSIS OF SENSORY-MOTOR CORTICAL GATING IN BEHAVING MICE
National Institutes of Health
Grant: 5R01NS084844-02
Vocal motor control and sensorimotor learning - behavior, muscles, and neurons
FWCI
1.09
Citation Percentile
0.7%
Citation Trend
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Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons