Temporal uncertainty interferes with the timely onset of evidence accumulation in perceptual decision making prompting the brain to rely instead on statistical regularities in the temporal structure of the environment.
For perceptual inference, human observers do not estimate sensory uncertainty instantaneously from the current sensory signals alone, but by combining past and current sensory inputs consistent with a Bayesian learner.
Reconstruction of great auk population dynamics suggests that hunting pressure alone could have been responsible for their extinction, demonstrating that even abundant, widespread species can be vulnerable to intense exploitation.
Delivering specific patterns of electrical activity to the median nerve of the arm triggers reliable sensations of texture, suggesting that it may ultimately be possible to restore complex tactile information to users of prosthetic limbs.
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.