Active Inference, homeostatic regulation and adaptive behavioural control.

TL;DR: In this article , the authors show that a minimal evolutionary framework is sufficient for small, low-level setpoints of metabolic homeostasis in cells to scale up into collectives (tissues) which solve a problem in morphospace: the organization of a body-wide positional information axis.

TL;DR: In this paper , the authors build upon empirical findings on the neurological syndrome of anosognosia for hemiplegia (lack of awareness into one's paralysis; AHP) and recent neuroscientific theories of self-awareness to propose that the experience of ones' self entails at least two normally integrated levels of inference.

TL;DR: A novel theoretical perspective on the brain’s unsupervised learning machinery that considers computational and neurobiological constraints, highlighting the connections between neuroscience and machine learning is offered.

TL;DR: This paper considers potential perturbation regimes for both naturalistic and virtual environments and discusses how AMs can be utilized to improve the understanding of both the theoretical basis of embodied interaction and the dynamic regulation of complex psychophysiological states.

TL;DR: It is proposed that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them, which provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task.

TL;DR: This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989), showing that Q-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action- values are represented discretely.

TL;DR: Findings in this work indicate that dopaminergic neurons in the primate whose fluctuating output apparently signals changes or errors in the predictions of future salient and rewarding events can be understood through quantitative theories of adaptive optimizing control.