A probabilistic framework is presented that enables image registration, tissue classification, and bias correction to be combined within the same generative model. A derivation of a log-likelihood objective function for the unified model is provided. The model is based on a mixture of Gaussians and is extended to incorporate a smooth intensity variation and nonlinear registration with tissue probability maps. A strategy for optimising the model parameters is described, along with the requisite partial derivatives of the objective function.
Abstract Energy homeostasis depends on behavior to predictively regulate metabolic states within narrow bounds. Here we review three theories of homeostatic control and ask how they provide insight into the circuitry underlying energy homeostasis. We offer two contributions. First, we detail how control theory and reinforcement learning are applied to homeostatic control. We show how these schemes rest on implausible assumptions; either via circular definitions, unprincipled drive functions, or by ignoring environmental volatility. We argue active inference can elude these shortcomings while retaining important features of each model. Second, we review the neural basis of energetic control. We focus on a subset of arcuate subpopulations that project directly to, and are thus in a privileged position to opponently modulate, dopaminergic cells as a function of energetic predictions over a spectrum of time horizons. We discuss how this can be interpreted under these theories, and how this can resolve paradoxes that have arisen. We propose this circuit constitutes a homeostatic-reward interface that underwrites the conjoint optimisation of physiological and behavioural homeostasis.
This technical note introduces a dynamic causal model (DCM) for resting state fMRI time series based upon observed functional connectivity—as measured by the cross spectra among different brain regions. This DCM is based upon a deterministic model that generates predicted crossed spectra from a biophysically plausible model of coupled neuronal fluctuations in a distributed neuronal network or graph. Effectively, the resulting scheme finds the best effective connectivity among hidden neuronal states that explains the observed functional connectivity among haemodynamic responses. This is because the cross spectra contain all the information about (second order) statistical dependencies among regional dynamics. In this note, we focus on describing the model, its relationship to existing measures of directed and undirected functional connectivity and establishing its face validity using simulations. In subsequent papers, we will evaluate its construct validity in relation to stochastic DCM and its predictive validity in Parkinson's and Huntington's disease.
Abstract Sensory processing is continuously shaped by internal bodily states, yet the neural mechanisms underlying this interoceptive-exteroceptive integration remain poorly understood. Predictive processing theories propose that bodily states modulate perceptual inference through precision-weighting (the contextual adjustment of prediction error gain according to sensory reliability), but empirical validation with neurobiologically realistic models has been lacking. We address this gap by combining cardiac phase-locked magnetoencephalography with systematic dynamic causal modelling to test competing mechanistic hypotheses about systolic-induced sensory attenuation. Using an auditory oddball paradigm, we observed selective suppression of deviant responses during cardiac systole (200-250ms post-stimulus), affecting prediction errors from unexpected tones whilst sparing expected tones. To identify the underlying synaptic mechanisms, we implemented three methodological innovations: (1) systematic comparison across 20 architectures representing precision-weighting (via intrinsic gains and/or modulatory connections), sensory gating (via forward connections), and predictive suppression (via backward connections) hypotheses; (2) Bayesian model reduction testing all 256 parameter configurations within the winning architecture to handle distributed model evidence; and (3) sensitivity analysis quantifying both direct effects and second-order interactions across the cortical hierarchy. Model comparison decisively favoured precision-weighting implementations (>99.99% posterior probability), with Bayesian model averaging revealing distributed gain control: superficial pyramidal self-inhibition in primary auditory cortex (94%) and inferior frontal gyrus (100%), inhibitory interneuron modulation in superior temporal gyrus (99%), and top-down modulatory connections from superior temporal to primary auditory cortex (96%). Critically, sensitivity analysis demonstrated that intrinsic inhibitory mechanisms exerted order-of-magnitude larger effects than hierarchical modulatory connections, with superior temporal gyrus emerging as an integration nexus showing extensive parameter interactions. These findings provide the first empirical validation of precision-weighting mechanisms during cardiac-sensory integration, establishing that systolic attenuation operates primarily through coordinated local inhibitory gain control rather than hierarchical (expected) attentional modulation. This modelling framework bridges computational theories of interoceptive-exteroceptive integration with laminar-specific cortical mechanisms, offering a generalisable methodology for testing predictive coding hypotheses about embodied perception.
Abstract Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. How-ever, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.
In the brain, the insular cortex receives a vast amount of interoceptive information, ascending through deep brain structures, from multiple visceral organs. The unique hierarchical and modular architecture of the insula suggests specialization for processing interoceptive afferents. Yet, the biological significance of the insula's neuroanatomical architecture, in relation to deep brain structures, remains obscure. In this opinion piece, we propose the Insula Hierarchical Modular Adaptive Interoception Control (IMAC) model to suggest that insula modules (granular, dysgranular and agranular), forming parallel networks with the prefrontal cortex and striatum, are specialized to form higher order interoceptive representations. These interoceptive representations are recruited in a context-dependent manner to support habitual, model-based and exploratory control of visceral organs and physiological processes. We discuss how insula interoceptive representations may give rise to conscious feelings that best explain lower order deep brain interoceptive representations, and how the insula may serve to defend the body and mind against pathological depression.
This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants—what we call “shared intelligence.” This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one’s sensed world—also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales, that is, inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent’s generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing—leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first—and key—step towards such an ecology.
