Evidence map›Paper›PMID 42858507›Full record

ReviewCurrent opinion in neurobiology2026

Receptive-field remapping and trans-saccadic space updating.

Ning Qian, Yiming Wang, Feng Wan, Min Jin, Mingsha Zhang

Abstract readReview
In one paragraph

Review in Current opinion in neurobiology, 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

5 authors.

Ning QianDepartment of Neuroscience and Zuckerman Institute, Columbia University, New York, NY, USA. Electronic address: nq6@columbia.edu.
Yiming WangDepartment of Neuroscience and Zuckerman Institute, Columbia University, New York, NY, USA; School of Life Sciences, Tsinghua University, Beijing, China.
Feng WanVassar College, Poughkeepsie, NY, USA.
Min JinState Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Mingsha ZhangState Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China. Electronic address: mingsha.zhang@bnu.edu.cn.

Funding

Physiological, Computational, and Psychological Approaches to Understanding Spatial VisionR01EY032938 · NEI · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI QIAN, NING · 2022 to 2025
$1.6M
NEI NIH HHS R01 EY032938
6 · The paper itself

Abstract

Since its discovery, perisaccadic receptive-field (RF) remapping-the shift of RFs around the time of saccades-has inspired many studies exploring its origins, characteristics, and functional implications. We review recent developments leading to a circuit model that links spatiotemporal properties of remapping to functional requirements of trans-saccadic visual stability. The model uses center/surround connections to store a stimulus' retinal position as a population activity. This activity profile is shifted backward (against the saccade) as a wave by directional connections gated by the corollary discharge of the saccade command. When the accumulative backward shift of the population activity equals the saccade magnitude (equivalent to forward RF remapping of the same magnitude), the pre-saccadic retinal position is updated to the correct post-saccadic position. The model explains translational mislocalization for stimuli flashed around saccades as imperfect updating. The model is equivalent to the classic head-direction model, suggesting a general strategy of neural computation.

Identifiers

PMID42858507
PMCPMC13660427

What OpenQuestion holds

Textmetadata
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.