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An inference paradigm for extracting neuronal correlations from two-photon imaging data, without requiring intermediate spike deconvolution, provides significant performance gains over existing methods as demonstrated by theoretical analysis, simulation studies, and real-data applications.

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.

New stimulus-dependencies of choice-related signals in sensory neurons uncovered by new analytical results and methods.

For many bacterial species, recombination dominates genome evolution and phylogenetic patterns that have so far been assumed to reflect clonal relationships, in fact reflect variation in recombination rates across lineages.

A proposed model of optimal inference under cognitive costs accounts for human sequential effects, including subtle patterns of attractive and repulsive influence of past observations, in a binary prediction task across a wide range of stimulus conditions.

The proposed techniques enable researchers to disentangle the statistical features of neural population responses, and rigorously quantify how these features carry information about stimuli and experimental variables.

Combined experimental and theoretical analysis identifies a molecular mechanism akin to working memory that enables single cells to perform complex navigation tasks in changing growth factor fields, beyond simple stimulus-response associations.

Validated mathematical relationships between motoneuron morphometric and electrophysiological properties are provided as a tool for neuroscientists and modellers to generate hypotheses for experimental studies investigating currently unreported relationships and build virtual motoneuron profiles with consistent properties for modelling purposes.

During perceptual decision making, maximizing total reward in the long term requires trading reward in the short term for a faster improvement in perceptual representations.

A new measure for cross-frequency coupling assesses phase-amplitude coupling and amplitude-amplitude coupling, and accounts for confounding factors such as low-frequency amplitude fluctuations, using a flexible statistical modeling approach.