Neural learning rules for generating flexible predictions and computing the successor representation is a research paper published in eLife (2022). On theSindex it has a DataRank of 1.7. It has been cited 61 times, with 55 citing works in its 1-hop citation network.
Scored on demand from live citation data
Repositories this paper deposited data in (declared in PubMed).
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.
Base Score Contribution
0.619
From this paper's citation signal
Citation Network Contribution
1.1
From 38 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 55 citers.
National Science Foundation
Grant: NeuroNex Award DBI-1707398
New York Stem Cell Foundation
Grant: Robertson Neuroscience Investigator Award
National Institutes of Health
Grant: NIH Director's New Innovator Award (DP2-AG071918)
Arnold and Mabel Beckman Foundation
Grant: Beckman Young Investigator Award
National Science Foundation
Grant: Graduate Research Fellowship Program
Simons Foundation
Grant: Society of Fellows
NIA NIH HHS
Grant: DP2 AG071918
National Institutes of Health
Grant: NIH Director's New Innovator Award (DP2-AG071918)
National Science Foundation
Grant: 1707398
NeuroNex Theory Team: Columbia University Theoretical Neuroscience Center
National Institutes of Health
Grant: 1DP2AG071918-01
Using a specialized behavior to study the neural mechanisms of episodic memory
Gatsby Charitable Foundation
Fields of Study
MeSH Terms
Keywords