Evidence map›Paper›PMID 40661128›Full record

ArticleAdvances in neural information processing systems2024

Model-based inference of synaptic plasticity rules.

Yash Mehta, Danil Tyulmankov, Adithya E Rajagopalan, Glenn C Turner, James E Fitzgerald, Jan Funke

Abstract read
In one paragraph

Article in Advances in neural information processing systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Flexible inference for animal learning rules using neural networks.Advances in neural information processing systems · 2025
    Article
  2. Review
  3. Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron.Advances in neural information processing systems · 2024
    Article
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.

Yash MehtaJanelia Research Campus, Howard Hughes Medical Institute.
Danil TyulmankovCenter for Theoretical Neuroscience, Columbia University.
Adithya E RajagopalanJanelia Research Campus, Howard Hughes Medical Institute.
Glenn C TurnerJanelia Research Campus, Howard Hughes Medical Institute.
James E FitzgeraldJanelia Research Campus, Howard Hughes Medical Institute.
Jan FunkeJanelia Research Campus, Howard Hughes Medical Institute.

Funding

Howard Hughes Medical Institute
6 · The paper itself

Abstract

Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function, employing either truncated Taylor series for theoretical interpretability or multilayer perceptrons. These plasticity parameters are optimized via gradient descent over entire trajectories to align closely with observed neural activity or behavioral learning dynamics. This method can uncover complex rules that induce long nonlinear time dependencies, particularly involving factors like postsynaptic activity and current synaptic weights. We validate our approach through simulations, successfully recovering established rules such as Oja's, as well as more intricate plasticity rules with reward-modulated terms. We assess the robustness of our technique to noise and apply it to behavioral data from

Identifiers

PMID40661128
PMCPMC12257422

What OpenQuestion holds

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LicenceCC BY
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.