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Bayesian inference identifies hidden dynamics underlying noisy cell division data.

Applying computational modeling to quantify threat learning processes uncovers how variations in these conserved learning processes relate to anxiety severity and the neuroanatomical substrates moderating these associations.

The fruit fly estimates visual motion by incorporating ON-OFF asymmetric processing that only improves performance when stimuli have light-dark asymmetries matched to natural scenes.

Feedback-driven gain modulation provides a mechanism to generate and maintain invariant sensory representations in the presence of contextual changes by dynamically adapting feedforward sensory processing.

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.

A comprehensive, data-driven and interpretable nonlinear computational modeling framework based on deep neural networks uncovers different nonlinear transformations of speech signal in the human auditory cortex.

Incorporating cycle-consistency loss into a generative adversarial network creates a high-performance and robust 'aligner' of neural population activity, permitting a fixed intracortical brain computer interface to be used for months without recalibration and without requiring inference of a latent manifold.

Recurrent neural network models enable prediction and design of health-relevant metabolite dynamics in synthetic human gut communities.

Model identification of neural encoding is an accessible system for the analysis of neural data that allows identifying and characterizing arbitrary relationships between neural activity and task-related variables such as behavior, stimuli, or internal states.

A hydrodynamic model of fish swimming in a channel predicts a critical flow speed for fish to successfully swim against a flow, unveiling a passive mechanism for rheotaxis to emerge without access to any sensory information.