Genephys is a generative model for dissecting the different aspects that compound our neural responses to perceptual stimulation, identifying which aspects remain stable and which ones vary across experimental repetitions.

A realistic model of the connections between local populations of neurons in the adult mouse brain can be constructed based on just two biologically plausible rules.

Single-cell data analysis must be guided by an underlying model.

A data-driven network model offers an interpretable and physiologically sound description of the whole-brain spontaneous neural activity of zebrafish larvae.

Oscillation component analysis enables cognitive neuroscientists to summarize millisecond-precision high-dimensional neurophysiological recordings into a smaller set of oscillatory components through biophysically inspired generative modeling of neural oscillations.

An iterative procedure using language models allows the generation of sequences from protein families, which score similarly to natural and experimentally validated sequences, with particular promise for small families.

Deep learning improves estimation of T cell receptor cohort frequencies and learns the rules of VDJ recombination, potentially making it helpful for vaccine design.

A nonlinear deep generative model can represent fetal–neonatal resting-state functional magnetic resonance imaging better than conventional linear models.

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.

A Bayesian model of phase angles illustrates a novel approach to the analysis of phase coherence in frequency-tagged experiments.