Can active inference model consciousness? We offer three conditions implying that it can. The first condition is the simulation of a reality or generative world model, which determines what can be known or acted upon; namely an epistemic field. The second is inferential competition to enter the world model. Only the inferences that coherently reduce long-term uncertainty win, evincing a selection for consciousness that we call Bayesian binding. The third is epistemic depth, which is the recurrent sharing of the Bayesian beliefs throughout the system. Due to this recursive loop — in a hierarchical system (such as a brain) — the world model contains the knowledge that it exists. This is distinct from self-consciousness, because the world model knows itself non-locally and continuously evidences this knowing (i.e., field-evidencing). Formally, we propose a hyper-model for precision-control across the entire hierarchy, whose latent states (or parameters) encode and control the overall structure and weighting rules for all layers of inference. This Beautiful Loop Theory is deeply revealing about meditation, psychedelic, and altered states, minimal phenomenal experience, and provides a new vision for conscious artificial intelligence.
[This corrects the article DOI: 10.3389/fpsyg.2018.02087.].
Article Informed spatial basis functions in minimum norm solutions for the electromagnetic source localisation problem. was published on January 1, 1999 in the journal Biomedical Engineering / Biomedizinische Technik (volume 44, issue s2).
Brief abstract Schizophrenia is hypothesized to be caused by impaired functional integration in the brain, and this could hypothetically be caused by white matter disruptions, synaptic dysfunction, or both. Neuroimaging studies consistently show reduced fractional anisotropy, a measure of white matter integrity, in patients with schizophrenia. Using Mendelian randomization, we show that these white matter changes are likely to be the consequence of a primary synaptopathy in schizophrenia.
Neuroimaging studies that investigate the involvement of brain regions in various cognitive and perceptual tasks have become increasingly prevalent. Functional magnetic resonance imaging (fMRI) studies are especially popular, due to their non-invasive nature and high spatial resolution. With recent advances in data analysis and modelling, it is now possible to use fMRI data to ask not only which brain regions are involved in these tasks, but also how they communicate with one another; for example, one can ask, “Is attentional modulation of visually evoked responses mediated by top-down (task-driven) or bottom-up (stimulus-driven) connections in the brain?” There are two state-of-the-art approaches for understanding the communication among distributed brain systems using neuroimaging. They reflect two distinct approaches to understanding connectivity. One approach—dynamic causal modelling (DCM)—tries to model how activity in one brain area is affected by activity in another (using models of effective connectivity), while the other—Granger causal modelling (GCM)—tests for the signature of these influences by looking for correlations in the activity of two or more regions (using models of functional connectivity). Previously, the relative accuracies of these methods, in disclosing patterns of communication among brain regions, were unknown. In a recent issue of PLoS Biology, Olivier David et al. compared them directly and provided evidence that may have a profound influence on their application [1]. Here, we consider the motivation behind the two techniques, their underlying assumptions, and the implications of David et al. [1] for their continued use.
This is the dataset accompanying the paper "Hierarchical generative modelling for autonomous robots" by Yuan et al.
This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium.
Deep learning's immense capabilities are often constrained by the complexity of its models, leading to an increasing demand for effective sparsification techniques.Bayesian sparsification for deep learning emerges as a crucial approach, facilitating the design of models that are both computationally efficient and competitive in terms of performance across various deep learning applications.The state-of-the-art -in Bayesian sparsification of deep neural networks -combines structural shrinkage priors on model weights with an approximate inference scheme based on stochastic variational inference.However, model inversion of the full generative model is exceptionally computationally demanding, especially when compared to standard deep learning of point estimates.In this context, we advocate for the use of Bayesian model reduction (BMR) as a more efficient alternative for pruning of model weights.As a generalization of the Savage-Dickey ratio, BMR allows a post-hoc elimination of redundant model weights based on the posterior estimates under a straightforward (non-hierarchical) generative model.Our comparative study highlights the advantages of the BMR method relative to established approaches based on hierarchical horseshoe priors over model weights.We illustrate the potential of BMR across various deep learning architectures, from classical networks like LeNet to modern frameworks such as Vision Transformers and MLP-Mixers.
