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.

Chromosomal instability of cancer can be quantitatively measured by phylogenetic analysis of 200 tumor cells while using evolutionary principles to account for cellular selection.

To make reliable but metabolically efficient perceptual inferences in a changing world, neural systems should dynamically adapt based on surprise and uncertainty about the sensory environment.

Emergent property inference, a novel machine learning methodology, learns distributions of neural circuit model parameters that produce computational properties and provides novel scientific insight through the quantification of the rich parametric structure it captures.

The complex histories of social relationships between enslaved and non-enslaved communities and their descendants during and after the Trans-Atlantic Slave-Trade shaped the detailed genetic and linguistic histories of admixture of the islands of Cabo Verde.

Explicit judgments of agency incorporate uncertainty by reflecting first-order measures of a noisy signal, but they do not correspond to second-order metacognitive measures of the noise in a signal.

New modelling, statistics, and experiments show that cellular populations of mitochondrial DNA (mtDNA) evolve during development according to solvable stochastic dynamics involving binomial partitioning and random turnover, facilitating a predictive and quantitative theory of the mtDNA bottleneck.

Deep neural networks can be trained to automatically find mechanistic models which quantitatively agree with experimental data, providing new opportunities for building and visualizing interpretable models of neural dynamics.

For analyzing time-dependent patch-clamp or patch-clamp fluorometry data of ion channels in terms of Markovian models, the superiority of Bayesian filtering with respect to traditional deterministic approaches is demonstrated enabling more reliable quantification of the parameters.

A mathematical model that combines stochasticity and spatial structure describes the dynamics of the viral population during an infection cycle, and fitting the model to RNA and virus abundances over time shows that poliovirus follows a geometric replication mode.