ArticleeLife2020
Training deep neural density estimators to identify mechanistic models of neural dynamics.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 112 papers.
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.
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.
Who cites it
112 citing papers in PubMed, 257 citations in OpenAlex.
- Probabilistic inference of homonymous and heteronymous recurrent inhibition in human muscles from large-scale motor neuron recordings.Science advances · 2026Article
- Modeling framework disentangles cerebellar mechanisms in speech feedback control, revealing trade-offs reshaped by degeneration.bioRxiv : the preprint server for biology · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- An integratediScience · 2026Article
- Simulation-based inference at the theoretical limit for fast, robust microstructural MRI with minimal diffusion data.Communications medicine · 2026Article
- Fast reconstruction of degenerate populations of conductance-based neuron models from spike times.PLoS computational biology · 2026Article
- Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver.bioRxiv : the preprint server for biology · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- Perilesional neuromodulation replaces lost sensorimotor function in persons with spinal cord injury.Nature biomedical engineering · 2026Article
- Who's afraid of synthetic data? Hybrid approaches to deliver medical digital twins.Informatics in medicine unlocked · 2026Article
- A Framework Integrating Spiking Cortical Circuit Modeling and Simulation-Based Inference to Probe Biomarkers of Cortical Dysfunction in Alzheimer's Disease.Interdisciplinary sciences, computational life sciences · 2026Article
- Attractor-Based Models for Sequences and Pattern Generation in Neural Circuits.Neural computation · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- Increased Perceptual Reliability Reduces Membrane Potential Variability in Cortical Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Neural posterior estimation on exponential random graph models: evaluating bias and implementation challenges.Statistics and computing · 2026Article
- Article
- Article
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Neuronal identity is not static: An input-driven perspective.PLoS computational biology · 2025Article
52 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 7 institutions in 3 countries.
Funding
Abstract
Mechanistic modeling in neuroscience aims to explain observed phenomena in terms of underlying causes. However, determining which model parameters agree with complex and stochastic neural data presents a significant challenge. We address this challenge with a machine learning tool which uses deep neural density estimators-trained using model simulations-to carry out Bayesian inference and retrieve the full space of parameters compatible with raw data or selected data features. Our method is scalable in parameters and data features and can rapidly analyze new data after initial training. We demonstrate the power and flexibility of our approach on receptive fields, ion channels, and Hodgkin-Huxley models. We also characterize the space of circuit configurations giving rise to rhythmic activity in the crustacean stomatogastric ganglion, and use these results to derive hypotheses for underlying compensation mechanisms. Our approach will help close the gap between data-driven and theory-driven models of neural dynamics.
Indexed as
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.