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.

A Broken Duet: Multistable Dynamics in Dyadic Interactions.

Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system-a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions.

Degeneracy in the neurological model of auditory speech repetition.

Both classic and contemporary models of auditory word repetition involve at least four left hemisphere regions: primary auditory cortex for processing sounds; pSTS (within Wernicke's area) for processing auditory images of speech; pOp (within Broca's area) for processing motor images of speech; and primary motor cortex for overt speech articulation. Previous functional-MRI (fMRI) studies confirm that auditory repetition activates these regions, in addition to many others. Crucially, however, contemporary models do not specify how regions interact and drive each other during auditory repetition.

Bistable perception, precision and neuromodulation.

Bistable perception follows from observing a static, ambiguous, (visual) stimulus with two possible interpretations. Here, we present an active (Bayesian) inference account of bistable perception and posit that perceptual transitions between different interpretations (i.e.

Reward Maximization Through Discrete Active Inference.

Active inference is a probabilistic framework for modeling the behavior of biological and artificial agents, which derives from the principle of minimizing free energy. In recent years, this framework has been applied successfully to a variety of situations where the goal was to maximize reward, often offering comparable and sometimes superior performance to alternative approaches. In this article, we clarify the connection between reward maximization and active inference by demonstrating how and when active inference agents execute actions that are optimal for maximizing reward.

Reclaiming saliency: Rhythmic precision-modulated action and perception.

Computational models of visual attention in artificial intelligence and robotics have been inspired by the concept of a saliency map. These models account for the mutual information between the (current) visual information and its estimated causes. However, they fail to consider the circular causality between perception and action.

How Active Inference Could Help Revolutionise Robotics.

Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference-a well-known description of sentient behaviour from neuroscience-can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems.

Bayesian Brains and the Rényi Divergence.

Under the Bayesian brain hypothesis, behavioral variations can be attributed to different priors over generative model parameters. This provides a formal explanation for why individuals exhibit inconsistent behavioral preferences when confronted with similar choices. For example, greedy preferences are a consequence of confident (or precise) beliefs over certain outcomes.

Cancer Niches and Their Kikuchi Free Energy.

Biological forms depend on a progressive specialization of pluripotent stem cells. The differentiation of these cells in their spatial and functional environment defines the organism itself; however, cellular mutations may disrupt the mutual balance between a cell and its niche, where cell proliferation and specialization are released from their autopoietic homeostasis. This induces the construction of cancer niches and maintains their survival.

Generative Models for Active Vision.

The active visual system comprises the visual cortices, cerebral attention networks, and oculomotor system. While fascinating in its own right, it is also an important model for sensorimotor networks in general. A prominent approach to studying this system is active inference-which assumes the brain makes use of an internal (generative) model to predict proprioceptive and visual input.

Simulating lesion-dependent functional recovery mechanisms.

Functional recovery after brain damage varies widely and depends on many factors, including lesion site and extent. When a neuronal system is damaged, recovery may occur by engaging residual (e.g.

Active Inference: Demystified and Compared.

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 limited work exists on comparing the two approaches on the same discrete-state environments. In this letter, we provide (1) an accessible overview of the discrete-state formulation of active inference, highlighting natural behaviors in active inference that are generally engineered in reinforcement learning, and (2) an explicit discrete-state comparison between active inference and reinforcement learning on an OpenAI gym baseline.

Paradoxical lesions, plasticity and active inference.

Paradoxical lesions are secondary brain lesions that ameliorate functional deficits caused by the initial insult. This effect has been explained in several ways; particularly by the reduction of functional inhibition, or by increases in the excitatory-to-inhibitory synaptic balance within perilesional tissue. In this article, we simulate how and when a modification of the excitatory-inhibitory balance triggers the reversal of a functional deficit caused by a primary lesion.

Active inference on discrete state-spaces: A synthesis.

Active inference is a normative principle underwriting perception, action, planning, decision-making and learning in biological or artificial agents. From its inception, its associated process theory has grown to incorporate complex generative models, enabling simulation of a wide range of complex behaviours. Due to successive developments in active inference, it is often difficult to see how its underlying principle relates to process theories and practical implementation.

Modules or Mean-Fields?

The segregation of neural processing into distinct streams has been interpreted by some as evidence in favour of a modular view of brain function. This implies a set of specialised 'modules', each of which performs a specific kind of computation in isolation of other brain systems, before sharing the result of this operation with other modules. In light of a modern understanding of stochastic non-equilibrium systems, like the brain, a simpler and more parsimonious explanation presents itself.

Neuromodulatory Control and Language Recovery in Bilingual Aphasia: An Active Inference Approach.

Understanding the aetiology of the diverse recovery patterns in bilingual aphasia is a theoretical challenge with implications for treatment. Loss of control over intact language networks provides a parsimonious starting point that can be tested using in-silico lesions. We simulated a complex recovery pattern (alternate antagonism and paradoxical translation) to test the hypothesis-from an established hierarchical control model-that loss of control was mediated by constraints on neuromodulatory resources.

Active listening.

This paper introduces active listening, as a unified framework for synthesising and recognising speech. The notion of active listening inherits from active inference, which considers perception and action under one universal imperative: to maximise the evidence for our (generative) models of the world. First, we describe a generative model of spoken words that simulates (i) how discrete lexical, prosodic, and speaker attributes give rise to continuous acoustic signals; and conversely (ii) how continuous acoustic signals are recognised as words.

Generative models, linguistic communication and active inference.

This paper presents a biologically plausible generative model and inference scheme that is capable of simulating communication between synthetic subjects who talk to each other. Building on active inference formulations of dyadic interactions, we simulate linguistic exchange to explore generative models that support dialogues. These models employ high-order interactions among abstract (discrete) states in deep (hierarchical) models.

Degeneracy and Redundancy in Active Inference.

The notions of degeneracy and redundancy are important constructs in many areas, ranging from genomics through to network science. Degeneracy finds a powerful role in neuroscience, explaining key aspects of distributed processing and structure-function relationships in the brain. For example, degeneracy accounts for the superadditive effect of lesions on functional deficits in terms of a "many-to-one" structure-function mapping.

Ongoing functional evolution of the bacterial atrazine chlorohydrolase AtzA.

Triazine herbicides such as atrazine and simazine which were heavily used in the latter half of the twentieth century constituted a rich new source of nitrogen for soil microbes. An atzA dechlorinase active against both atrazine and simazine was isolated from various soil bacteria from diverse locations in the mid 1990s. We have surveyed the atzA genes from eight triazine-degrading Aminobacter aminovorans strains isolated from French agricultural soils recurrently exposed to triazines in 2000.

Intramolecular epistasis and the evolution of a new enzymatic function.

Atrazine chlorohydrolase (AtzA) and its close relative melamine deaminase (TriA) differ by just nine amino acid substitutions but have distinct catalytic activities. Together, they offer an informative model system to study the molecular processes that underpin the emergence of new enzymatic function. Here we have constructed the potential evolutionary trajectories between AtzA and TriA, and characterized the catalytic activities and biophysical properties of the intermediates along those trajectories.

Structural bioinformatics of Vibrio cholerae aminopeptidase A (PepA) monomer.

Aminopeptidase A (PepA) is a metalloexopeptidase found in Vibrio cholerae .It functions as a transcriptional repressor in regulatory cascade that controls virulence gene expression in V. cholerae.

Homology modeling of hemagglutinin/protease [HA/P (vibriolysin)] from Vibrio cholerae: sequence comparision, residue interactions and molecular mechanism.

Vibrio cholerae produces a zinc-containing and calcium-stabilized soluble hemagglutinin/protease, which has been earlier shown to have the ability to cleave several physiologically important substrates including mucin, fibronectin and lactoferin. This study presents homology modeling of hemagglutinin/protease (vibriolysin) from Vibrio cholerae in the presence of inhibitor HPI [N-(1-carboxy-3-phenylpropyl)-phenylalanyl-alpha-aspargine]. The 3D structure was predicted based on its sequence homology with Pseudomonas aeruginosa elastase (PAE).

Characterization of protomer interfaces in HslV protease; the bacterial homologue of 20S proteasome.

HslVU, a two-component proteasome-related prokaryotic system is composed of HslV protease and HslU ATPase. HslV protomers assemble in a dodecamer of two-stacked hexameric rings that form a complex with HslU hexamers. The intra- and inter-ring protomer interfaces in the HslV dodecamer underpin the integrity and functionality of HslVU.