Evidence map›Paper›PMID 42146698›Full record

ArticlebioRxiv : the preprint server for biology2026

A UNIFORM CODING STRUCTURE IN THE NEOCORTEX.

Mohamed Badawy, Alon Amir, Mohammad M Herzallah, Ian T Kim, Drew B Headley, Denis Paré

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 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

6 authors.

Mohamed BadawyCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Alon AmirCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Mohammad M HerzallahCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Ian T KimCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Drew B HeadleyCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.
Denis ParéCenter for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102.

Funding

SIGNALING OF SALIENCE AND PREDICTION ERRORS BY THE INSULAR01MH130331 · NIMH · RUTGERS THE STATE UNIV OF NJ NEWARK · PI DENIS PARE · 2023 to 2026
$1.6M
NIMH NIH HHS R01 MH130331
6 · The paper itself

Abstract

Prefrontal neurons simultaneously encode multiple task variables. While many studies reported that various groupings of task features could be detected at the population level, the combination of features encoded by individual neurons seemed random. Here, based on unit recordings with Neuropixel probes in behaving rats, we report that far from being random, the representation of information is highly structured. Specifically, the prefrontal network exhibits multiple coding gradients orthogonal to each other in a multidimensional representational space. In this coding structure, neurons have correlated absolute firing rate modulations by different variables, but the polarity of the modulation by one variable is not predictive of that by others. Moreover, this coding structure is manifest in tasks that probe different behavioral processes, ranging from defensive behaviors to sensory discrimination. Last, we find that the same structured representation is apparent in other neocortical regions, including associative and primary sensory areas.

Indexed as

active avoidanceCorticaldefensive behaviorsfreezingparietalprefrontalrepresentationsensory discriminationsomatosensoryUMAPvisual

Identifiers

PMID42146698
PMCPMC13174290

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.