A differentiable variant of the Gillespie algorithm enables gradient-based optimization for stochastic chemical kinetics, facilitating efficient parameter estimation and the design of biochemical networks with desired input–output relationships.

A new machine learning method makes it possible to efficiently identify the parameters of cognitive models using Bayesian inference, even when only model simulations are available.

A combined experimental and modeling approach provides insight into potential biases when inferring transcription rates from static mRNA distributions, and shows that correcting for cell-cycle phase and post-transcriptional noise provides rates that agree with live-cell transcription measurements.

A novel method and software provides researchers with the capability to rapidly, flexibly, and robustly perform Bayesian parameter estimation of theoretically meaningful models in cognitive neuroscience that were heretofore intractable.

Increased usability and validity of neuroscience models, through FAIR workflows for the whole modeling process, including data and model management, parameter estimation, uncertainty quantification, and model analysis.

Leave-One-Trial-Out (LOTO) is a general, efficient and easily implementable approach for inferring trial-by-trial measures of computational model parameters in order to link these measures to neural mechanisms.

µGUIDE is a Bayesian framework that leverages simulation-based inference to efficiently estimate posterior distributions of any forward model parameters, allowing for uncertainty quantification and degeneracy detection.

A novel approach together with a user-friendly web-based analytic tool, coined 'REAP', was proposed to improve the quantitative assessment of dose-response relationship and drug potency.

This first comparison of different methodologies to estimate germline mutation rate from pedigrees resulted in almost a 2-fold variation in the estimated rates, discrepancies mainly caused by different filtering methods.

Neurons differ in their impact on collective cortical activity, with sensitive neurons forming a stable topological core, implicated in cortical-state transitions, while peripheral insensitive neurons are more responsive to stimuli.