Evidence map›Paper›PMID 38413233›Full record

ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2024

Recurrent Neural Circuits Overcome Partial Inactivation by Compensation and Re-learning.

Colin Bredenberg, Cristina Savin, Roozbeh Kiani

Abstract read
In one paragraph

Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2024. 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. Review
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

3 authors.

Colin BredenbergCenter for Neural Science, New York University, New York, NY 10003.
Cristina SavinCenter for Neural Science, New York University, New York, NY 10003 roozbeh@nyu.edu.
Roozbeh KianiCenter for Neural Science, New York University, New York, NY 10003 roozbeh@nyu.edu.ORCID 0000-0003-0614-6791

Funding

Causal power of cortical neural ensembles: mechanisms and utility for brain perturbationsR01MH127375 · NIMH · NEW YORK UNIVERSITY · PI Roozbeh Kiani, Luca Mazzucato · 2022 to 2026
$3.0M
CRCNS: Inferring reference points from OFC population dynamicsR01MH125571 · NIMH · NEW YORK UNIVERSITY · PI CONSTANTINOPLE, CHRISTINE M · 2020 to 2024
$1.7M
CRCNS: Neural coding and computation in large ensembles in prefrontal cortexR01MH109180 · NIMH · NEW YORK UNIVERSITY · PI KIANI, ROOZBEH · 2015 to 2018
$939k
NIMH NIH HHS R01 MH109180NIMH NIH HHS R01 MH125571NIMH NIH HHS R01 MH127375
6 · The paper itself

Abstract

Technical advances in artificial manipulation of neural activity have precipitated a surge in studying the causal contribution of brain circuits to cognition and behavior. However, complexities of neural circuits challenge interpretation of experimental results, necessitating new theoretical frameworks for reasoning about causal effects. Here, we take a step in this direction, through the lens of recurrent neural networks trained to perform perceptual decisions. We show that understanding the dynamical system structure that underlies network solutions provides a precise account for the magnitude of behavioral effects due to perturbations. Our framework explains past empirical observations by clarifying the most sensitive features of behavior, and how complex circuits compensate and adapt to perturbations. In the process, we also identify strategies that can improve the interpretability of inactivation experiments.

Indexed as

LearningNeuronsCognitionNeural Networks, ComputerProblem Solvingcircuit perturbationcompensationfunctional integrityrecurrent neural networksredundant architecturere-learning

Identifiers

PMID38413233
PMCPMC11026338

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.