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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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4 results for “Hodgkin-Huxley”

Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Datastat.ML

VIND infers smooth nonlinear dynamics from electrophysiology data.

problem Analyzing smooth, nonlinear time series data from neuroscience experiments.

method Variational Inference for Nonlinear Dynamics (VIND) with structured approximate posterior and fixed-point iteration.

result VIND reconstructs 5D latent space variables similar to Hodgkin-Huxley models, and excels in predicting future neural activity.

Variational Inference Nonlinear Dynamics Electrophysiology Neural Activity Hodgkin-Huxley

2018-11-06abs ↗pdf ↗

Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimationstat.ML

ACE improves GBI for simulators by approximating cost functions, making inference more efficient.

problem Inference for misspecified simulators is overly restrictive.

method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).

result ACE provides accurate cost predictions and more efficient inference.

simulation-based inference Bayesian inference misspecified models amortized cost estimation Hodgkin-Huxley model

2023-05-24abs ↗pdf ↗

Data-Driven Predictive Modeling of Neuronal Dynamics using Long Short-Term Memoryq-bio.NC

Develops LSTM for predicting neuronal dynamics over long time-horizons.

problem Understanding and controlling complex brain behaviors.

method Long Short-Term Memory (LSTM) neural network architecture for multi-time step predictions.

result LSTM improves short time-horizon prediction accuracy and multi-time step predictions of neuronal dynamics.

Neural Networks Long Short-Term Memory Predictive Modeling Neuronal Dynamics Hippocampal Pyramidal Neurons

2019-08-11abs ↗pdf ↗

Learning High-dimensional Ionic Model Dynamics Using Fourier Neural Operatorscs.LG

Fourier Neural Operators accurately predict dynamics of high-dimensional ionic models.

problem Approximating stiff, multiscale ionic models using neural networks.

method Fourier Neural Operators for learning dynamics of high-dimensional ionic models.

result Fourier Neural Operators can accurately predict dynamics of high-dimensional ionic models.

Fourier Neural Operators Ionic Models Stiff ODEs Artificial Neural Networks High-dimensional Dynamics

2025-05-20abs ↗pdf ↗