Generative models for sequential dynamics in active inference.

A central theme of theoretical neurobiology is that most of our cognitive operations require processing of discrete sequences of items. This processing in turn emerges from continuous neuronal dynamics. Notable examples are sequences of words during linguistic communication or sequences of locations during navigation.

Inferring when to move.

Most of our movement consists of sequences of discrete actions at regular intervals-including speech, walking, playing music, or even chewing. Despite this, few models of the motor system address how the brain determines the interval at which to trigger actions. This paper offers a theoretical analysis of the problem of timing movements.

Supervised structure learning.

This paper concerns structure learning or discovery of discrete generative models. It focuses on Bayesian model selection and the assimilation of training data or content, with a special emphasis on the order in which data are ingested. A key move-in the ensuing schemes-is to place priors on the selection of models, based upon expected free energy.

Effects of feeding earthworm or vermicompost on early life performance of broilers under challenging dietary conditions.

We investigated if feeding earthworms (EW) or vermicompost (VC) to broilers improves performance and aids in coping with dietary challenges from a soluble non-starch polysaccharide (NSP)-enriched diet (negative control diet; CON-). Newly-hatched male Cobb-500 birds (N = 480) were fed either a positive (+) control diet (CON+, n = 240) or CON+ supplemented with either 1% EW (CON+EW; n = 120) or 1% VC in DM (CON+VC; n = 120) for 8 d (Period 1; P1). At the end of P1, blood and intestinal samples were taken from half the birds in each group.

The Many Roles of Precision in Action.

Active inference describes (Bayes-optimal) behaviour as being motivated by the minimisation of surprise of one's sensory observations, through the optimisation of a generative model (of the hidden causes of one's sensory data) in the brain. One of active inference's key appeals is its conceptualisation of precision as biasing neuronal communication and, thus, inference within generative models. The importance of precision in perceptual inference is evident-many studies have demonstrated the importance of ensuring precision estimates are correct for normal (healthy) sensation and perception.