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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Neural dynamics and brain function
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

4,098 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
4,098 works in the cohort · of 4,299,418page 1 of 82

Labels cover 0 of 4,098 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 4,098 of 4,098 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

afffundno abstractunlabeled
A deep learning framework for neuroscience
Blake A. Richards, Timothy Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafał Bogacz, Amelia J. Christensen +26 more
2019· review· en· Nature Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
1,087
citations
afffundunlabeled
A Large-Scale Model of the Functioning Brain
Chris Eliasmith, Terrence C. Stewart, Xuan Choo, Trevor Bekolay, Travis DeWolf, Yichuan Tang +1 more
2012· article· en· Science· Neuroscience
machine prediction:candidate · noneconsensus · none
963
citations
affno abstractunlabeled
Communication dynamics in complex brain networks
Andrea Avena‐Koenigsberger, Bratislav Mišić, Olaf Sporns
2017· review· en· Nature reviews. Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
944
citations
affno abstractunlabeled
Backpropagation and the brain
Timothy Lillicrap, Adam Santoro, Luke Marris, Colin J. Akerman, Geoffrey E. Hinton
2020· review· en· Nature reviews. Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
924
citations
affunlabeled
Time-resolved resting-state brain networks
Andrew Zalesky, Alex Fornito, Luca Cocchi, Leonardo L. Gollo, Michael Breakspear
2014· article· en· Proceedings of the National Academy of Sciences· Neuroscience
machine prediction:candidate · noneconsensus · none
838
citations
affno abstractunlabeled
Theories of consciousness
Anil K. Seth, Tim Bayne
2022· review· en· Nature reviews. Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
823
citations
affno abstractunlabeled
Information processing with population codes
Alexandre Pouget, Peter Dayan, Richard S. Zemel
2000· review· en· Nature reviews. Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
782
citations
affno abstractunlabeled
Neuronal variability: noise or part of the signal?
Richard B. Stein, E. Roderich Gossen, Kelvin E. Jones
2005· review· en· Nature reviews. Neuroscience· Neuroscience
machine prediction:candidate · noneconsensus · none
693
citations
affunlabeled
No one knows what attention is
Bernhard Hommel, Craig S. Chapman, Paul Cisek, Heather F. Neyedli, Joo‐Hyun Song, Timothy N. Welsh
2019· review· en· Attention Perception & Psychophysics· Neuroscience
machine prediction:candidate · noneconsensus · none
597
citations

How this was built: Screen · Findings · About