Successful speech perception requires that listeners map the acoustic signal to linguistic
categories. These mappings are not only probabilistic, but change depending on the …

Brain function involves the activity of neuronal populations. Much recent effort has been
devoted to measuring the activity of neuronal populations in different parts of the brain under …

Active inference is a first principle account of how autonomous agents operate in dynamic,
nonstationary environments. This problem is also considered in reinforcement learning, but …

There is strong behavioral and physiological evidence that the brain both represents
probability distributions and performs probabilistic inference. Computational neuroscientists …

The idea that the brain learns generative models of the world has been widely promulgated.
Most approaches have assumed that the brain learns an explicit density model that assigns …

It is widely believed that the brain performs approximate probabilistic inference to estimate
causal variables in the world from ambiguous sensory data. To understand these …

Over the last decade, various spiking neural network models have been proposed, along
with a similarly increasing interest in spiking models of computation in computational …

Bayesian decision theory is a mathematical framework that models reasoning and decision‐
making under uncertain conditions. The past few decades have witnessed an explosion of …

Bayesian brain theory suggests that the brain employs generative models to understand the
external world. The sampling-based perspective posits that the brain infers the posterior …

The olfactory system faces a hard problem: on the basis of noisy information from olfactory
receptor neurons (the neurons that transduce chemicals to neural activity), it must figure out …