Evidence map›Paper›PMID 41542162›Full record

ArticleArXiv2026

Hierarchical temporal receptive windows and zero-shot timescale generalization in biologically constrained scale-invariant deep networks.

Aakash Sarkar, Marc W Howard

Abstract readPreprint
In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Aakash SarkarDepartment of Psychological and Brain Sciences, Boston University.
Marc W HowardDepartment of Psychological and Brain Sciences, Department of Physics, Boston University.

Funding

CRCNS: Neural Representations of Time Across Scales in Natural and Artificial NetworksR01MH132171 · NIMH · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI HOWARD, MARC W · 2022 to 2024
$1.3M
NIMH NIH HHS R01 MH132171
6 · The paper itself

Abstract

Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on a language classification task mimicking the hierarchical structure of language (e.g.,

Identifiers

PMID41542162
PMCPMC12803329

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.