The Anatomy of Inference: Generative Models and Brain Structure.
Thomas Parr,Karl J. Friston +1 more
TLDR
It is argued that the form of the generative models required for inference constrains the way in which brain regions connect to one another, and is illustrated in four different domains: perception, planning, attention, and movement.Abstract:
To infer the causes of its sensations, the brain must call on a generative (predictive) model. This necessitates passing local messages between populations of neurons to update beliefs about hidden variables in the world beyond its sensory samples. It also entails inferences about how we will act. Active inference is a principled framework that frames perception and action as approximate Bayesian inference. This has been successful in accounting for a wide range of physiological and behavioural phenomena. Recently, a process theory has emerged that attempts to relate inferences to their neurobiological substrates. In this paper, we review and develop the anatomical aspects of this process theory. We argue that the form of the generative models required for inference constrains the way in which brain regions connect to one another. Specifically, neuronal populations representing beliefs about a variable must receive input from populations representing the Markov blanket of that variable. We illustrate this idea in four different domains: perception, planning, attention, and movement. In doing so, we attempt to show how appealing to generative models enables us to account for anatomical brain architectures. Ultimately, committing to an anatomical theory of inference ensures we can form empirical hypotheses that can be tested using neuroimaging, neuropsychological, and electrophysiological experiments.read more
Citations
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Amygaloid Projections To The Mesencephalon, Pons And Medulla Oblongata In The Cat
G. Holstege,D.A. Hopkins +1 more
TL;DR: The location of the cells of origin and the distribution of the terminals of the amygdalotegmental projection suggest that this pathway plays an important role in the integration of somatic and autonomic responses associated with affective defense.
Journal ArticleDOI
Active inference on discrete state-spaces: A synthesis.
Lancelot Da Costa,Lancelot Da Costa,Thomas Parr,Noor Sajid,Sebastijan Veselic,Victorita Neacsu,Karl J. Friston +6 more
TL;DR: A complete mathematical synthesis of active inference on discrete state-space models, which derives neuronal dynamics from first principles and relates this dynamics to biological processes is provided.
Journal ArticleDOI
Deep active inference as variational policy gradients
TL;DR: This paper proposes a novel deep Active Inference algorithm which approximates key densities using deep neural networks as flexible function approximators, which enables active Inference to scale to significantly larger and more complex tasks.
Journal ArticleDOI
From allostatic agents to counterfactual cognisers: active inference, biological regulation, and the origins of cognition
TL;DR: Godfrey-Smith as mentioned in this paper argued that biological cognition is rooted in a particular kind of functional organisation; namely, that which enables the agent to detach from the present and engage in counterfactual (active) inference.
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An Integrated World Modeling Theory (IWMT) of Consciousness: Combining Integrated Information and Global Neuronal Workspace Theories With the Free Energy Principle and Active Inference Framework; Toward Solving the Hard Problem and Characterizing Agentic Causation.
TL;DR: This work describes how streams of consciousness may emerge as an evolving generation of sensorimotor predictions, with the precise composition of experiences depending on the integration abilities of synchronous complexes as self-organizing harmonic modes (SOHMs).
References
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