ArticleNature machine intelligence2020
Moving beyond generalization to accurate interpretation of flexible models.
Article in Nature machine intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
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The trial behind it
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Who cites it
17 citing papers in PubMed.
- Application of Machine Learning Models to Identify Differences in Neural Electrophysiological Properties Across Estrous Cycle Phases.Neuroinformatics · 2026Article
- A doubly stochastic renewal framework for partitioning spiking variability.Nature communications · 2025Article
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- Latent circuit inference from heterogeneous neural responses during cognitive tasks.Nature neuroscience · 2025Article
- Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.Science advances · 2024Article
- Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.Advances in neurobiology · 2024Review
- Editorial: Computational modelling of cardiovascular hemodynamics and machine learning.Frontiers in cardiovascular medicine · 2024Article
- Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.Advances in neurobiology · 2024Review
- Predictive variational autoencoder for learning robust representations of time-series data.ArXiv · 2023Article
- The dynamics and geometry of choice in premotor cortex.bioRxiv : the preprint server for biology · 2023Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Inferring structural and dynamical properties of gene networks from data with deep learning.NAR genomics and bioinformatics · 2022Article
- Review
- A flexible Bayesian framework for unbiased estimation of timescales.Nature computational science · 2022Article
- Metastable dynamics of neural circuits and networks.Applied physics reviews · 2022Review
- Learning non-stationary Langevin dynamics from stochastic observations of latent trajectories.Nature communications · 2021Article
- Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Machine learning optimizes flexible models to predict data. In scientific applications, there is a rising interest in interpreting these flexible models to derive hypotheses from data. However, it is unknown whether good data prediction guarantees accurate interpretation of flexible models. Here we test this connection using a flexible, yet intrinsically interpretable framework for modelling neural dynamics. We find that many models discovered during optimization predict data equally well, yet they fail to match the correct hypothesis. We develop an alternative approach that identifies models with correct interpretation by comparing model features across data samples to separate true features from noise. We illustrate our findings using recordings of spiking activity from the visual cortex of behaving monkeys. Our results reveal that good predictions cannot substitute for accurate interpretation of flexible models and offer a principled approach to identify models with correct interpretation.
Identifiers
What OpenQuestion holds
Registered trials
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.