ArticleNature methods2025
JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Distribution and voltage dependence of ion channels shape single-neuron computations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A computational model of altered neuronal activity in altered gravity.NPJ microgravity · 2026Article
- An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity.PLoS computational biology · 2026Article
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- What can a neuron compute.bioRxiv : the preprint server for biology · 2026Article
- Neuronal excitability and parameter variability in the Hodgkin-Huxley model.PLoS computational biology · 2026Article
- Linking reduced prefrontal microcircuit inhibition in schizophrenia to EEG biomarkers in silico.PLoS computational biology · 2026Article
- Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function.bioRxiv : the preprint server for biology · 2026Article
- The Neural Analysis Toolkit Unifies Semi-Analytical Techniques to Simplify, Understand, and Simulate Dendrites.Neuroinformatics · 2026Article
- Alzheimer's Pathology Enhances Excitatory Synaptic Input and Integration in VTA Dopamine Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
- AI-Resolved Protein Energy Landscapes, Electrodynamics, and Fluidic Microcircuits as a Unified Framework for Predicting Neurodegeneration.International journal of molecular sciences · 2026Review
- Container-based framework for large-scale spiking network simulation.Frontiers in neuroscience · 2026Article
- Going deeper with morphologically detailed neural networks by simulation-based gradient propagation.Frontiers in computational neuroscience · 2026Article
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- Differentiable simulation expands frontiers for biophysical neural models.Nature methods · 2025Article
- PKC-dependent enhancement of glutamate input to VTA dopamine neurons in 3xTg-AD mice.bioRxiv : the preprint server for biology · 2025Article
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11 authors.
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Abstract
Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perform computational tasks. Here we describe a framework for simulating biophysical models in neuroscience-JAXLEY-which addresses this challenge. By making use of automatic differentiation and GPU acceleration, JAXLEY enables optimizing large-scale biophysical models with gradient descent. JAXLEY can learn biophysical neuron models to match voltage or two-photon calcium recordings, sometimes orders of magnitude more efficiently than previous methods. JAXLEY also makes it possible to train biophysical neuron models to perform computational tasks. We train a recurrent neural network to perform working memory tasks, and a network of morphologically detailed neurons with 100,000 parameters to solve a computer vision task. JAXLEY improves the ability to build large-scale data- or task-constrained biophysical models, creating opportunities for investigating the mechanisms underlying neural computations across multiple scales.
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