Evidence map›Paper›PMID 36993218›Full record

ArticlebioRxiv : the preprint server for biology2023

Bipartite invariance in mouse primary visual cortex.

Zhiwei Ding, Dat T Tran, Kayla Ponder, Erick Cobos, Zhuokun Ding, Paul G Fahey, Eric Wang, Taliah Muhammad, Jiakun Fu, Santiago A Cadena and 8 more

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Zhiwei DingCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Dat T TranCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Kayla PonderCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Erick CobosCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Zhuokun DingCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Paul G FaheyCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Eric WangCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Taliah MuhammadCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Jiakun FuCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Santiago A CadenaMax Planck Institute for Dynamics and Self-Organization, Göttingen, Germany.
Stelios PapadopoulosCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Saumil PatelCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Katrin FrankeCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Jacob ReimerCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Fabian H SinzCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Alexander S EckerInstitute of Computer Science and Campus Institute Data Science, University of Göttingen, Germany.
Xaq PitkowCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.
Andreas S ToliasCenter for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX, USA.

Funding

Generation of novel cell type specific mouse genetic toolsU19MH114830 · NIMH · ALLEN INSTITUTE · PI NGAI, JOHN J. · 2017 to 2021
$64.7M
VISION RESEARCH CENTERP30EY002520 · NEI · BAYLOR COLLEGE OF MEDICINE · PI Samuel M Wu · 1985 to 2026
$14.8M
NEI NIH HHS P30 EY002520NIMH NIH HHS U19 MH114830
6 · The paper itself

Abstract

A defining characteristic of intelligent systems, whether natural or artificial, is the ability to generalize and infer behaviorally relevant latent causes from high-dimensional sensory input, despite significant variations in the environment. To understand how brains achieve generalization, it is crucial to identify the features to which neurons respond selectively and invariantly. However, the high-dimensional nature of visual inputs, the non-linearity of information processing in the brain, and limited experimental time make it challenging to systematically characterize neuronal tuning and invariances, especially for natural stimuli. Here, we extended "inception loops" - a paradigm that iterates between large-scale recordings, neural predictive models, and

Identifiers

PMID36993218
PMCPMC10055119

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.