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Being You: A New Science of Consciousness is a 2021 non-fiction book by neuroscientist Anil Seth, published by Faber and Faber. The book explores the author's theory of consciousness and the self. Seth also looks at the relationship between humans, animals, and the potential for machines to have consciousness.
Models of consciousness are used to illustrate and aid in understanding and explaining distinctive aspects of consciousness. Sometimes the models are labeled theories of consciousness . Anil Seth defines such models as those that relate brain phenomena such as fast irregular electrical activity and widespread brain activation to properties of ...
Anil Kumar Seth (born 11 June 1972) is a British neuroscientist and professor of Cognitive and Computational Neuroscience at the University of Sussex. A proponent of materialist explanations of consciousness , [ 1 ] he is currently amongst the most cited scholars on the topics of neuroscience and cognitive science globally.
Neuroscientist and consciousness researcher Anil Seth is supportive of the theory, with some caveats, claiming that "conscious experiences are highly informative and always integrated."; and that "One thing that immediately follows from [IIT] is that you have a nice post hoc explanation for certain things we know about consciousness.".
The information integration theory of consciousness (IITC) shares with the DCH the idea that conscious experiences provide informative discriminations among a vast repertoire of possible experiences. In the IITC, the quantity phi is defined as the information that is integrated across the informational "weakest link" of a system.
Orchestrated objective reduction (Orch OR) is a controversial theory postulating that consciousness originates at the quantum level inside neurons (rather than being a product of neural connections). The mechanism is held to be a quantum process called objective reduction that is orchestrated by cellular structures called microtubules .
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This idea is also present in many other theories of neural learning, such as sparse coding, with the central difference being that in predictive coding not only the connections to sensory inputs are learned (i.e., the receptive field), but also top-down predictive connections from higher-level representations.