The brain is not mental! coupling neuronal and immune cellular processing in human organisms

Ciaunica, Anna; Shmeleva, Evgeniya V.; Levin, Michael

doi:10.3389/fnint.2023.1057622

Cited by 12 publications

(6 citation statements)

References 168 publications

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“…Specifically, we have argued that regulative morphogenesis is a kind of behavior of cellular collectives traversing anatomical morphospace (Fig. 1f, g) [27][28][29][30][31][32] , and others have argued that immune systems 33,34 , bacterial biofilms [35][36][37][38] , and many other unconventional substrates [39][40][41][42] can be effectively understood and rationally controlled by using techniques from behavioral and cognitive science 43 .…”

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confidence: 99%

Collective intelligence: A unifying concept for integrating biology across scales and substrates

McMillen,

Levin

2024

Commun Biol

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A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.

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confidence: 99%

Collective intelligence: A unifying concept for integrating biology across scales and substrates

McMillen,

Levin

2024

Commun Biol

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“…Self-organization entered the scientific arena within first-order cybernetics in the 1940s-1950s and was later extended to physics, biology, and neuroscience [50,54,72]. Additionally, it can be defined by the notions of system autonomy.…”

Section: The Progress In the Systems Theories From The Mid-20th Centu...mentioning

confidence: 99%

“…Additionally, it can be defined by the notions of system autonomy. Whether as a stand-alone theory explaining complex patterns in the world (see below self-organizing criticality) or as part of other theories, such as the autopoiesis theory of Varela and Maturana, selforganization points to the autonomous systems that are "Organizationally closed, so that the network of processes is recursively interdependent in the generation and realization of the processes themselves" [72], (p. 2), [73] This closure in production and space, the two premises of autopoiesis, seriously challenges reductionist efforts to explain the higher organization of matter in biological systems solely in terms of component interactions with lower levels. Why?…”

Section: The Progress In the Systems Theories From The Mid-20th Centu...mentioning

confidence: 99%

Universal Complexity Science and Theory of Everything: Challenges and Prospects

Kesić

2024

Systems

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This article argues that complexity scientists have been searching for a universal complexity in the form of a “theory of everything” since some important theoretical breakthroughs such as Bertalanffy’s general systems theory, Wiener’s cybernetics, chaos theory, synergetics, self-organization, self-organized criticality and complex adaptive systems, which brought the study of complex systems into mainstream science. In this respect, much attention has been paid to the importance of a “reductionist complexity science” or a “reductionist theory of everything”. Alternatively, many scholars strongly argue for a holistic or emergentist “theory of everything”. The unifying characteristic of both attempts to account for complexity is an insistence on one robust explanatory framework to describe almost all natural and socio-technical phenomena. Nevertheless, researchers need to understand the conceptual historical background of “complexity science” in order to understand these longstanding efforts to develop a single all-inclusive theory. In this theoretical overview, I address this underappreciated problem and argue that both accounts of the “theory of everything” seem problematic, as they do not seem to be able to capture the whole of reality. This realization could mean that the idea of a single omnipotent theory falls flat. However, the prospects for a “holistic theory of everything” are much better than a “reductionist theory of everything”. Nonetheless, various forms of contemporary systems thinking and conceptual tools could make the path to the “theory of everything” much more accessible. These new advances in thinking about complexity, such as “Bohr’s complementarity”, Morin’s Complex thinking, and Cabrera’s DSRP theory, might allow the theorists to abandon the EITHER/OR logical operators and start thinking about BOTH/AND operators to seek reconciliation between reductionism and holism, which might lead them to a new “theory of everything”.

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“…Instead of an environment, we describe how to complete a task, with cognition being enacted through that task. After all, cognition involves more than the brain [45], it extends through the body and into the environment [39]. For example, spider cognition extends into their webs [46].…”

Section: Tasksmentioning

confidence: 99%

The Phenomenal Is Functional: A Unified Theory of Consciousness and Computation

Bennett,

Welsh

2023

Preprint

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Please note this is a draft on which we are seeking feedback. Substantial changes and a third author are likely be added to the next version. The hard problem of consciousness asks why there is something it is like to be a conscious organism. We address this from 1st principles, by constructing a formalism that unifies lower and higher order theories of consciousness. We assume pancomputationalism and hold that the environment learns organisms that exhibit fit behaviour via the algorithm we call natural selection. Selection learns organisms that learn to classify causes, facilitating adaptation. Recent experimental and mathematical computer science elucidates how. Scaling this capacity implies a progressively higher order of ``causal identity'' facilitating reafference and P-consciousness, then self awareness and A-consciousness, and then meta self awareness. We then use this to resolve the hard problem in precise terms. First, we deny that a philosophical zombie is in all circumstances as capable as a P-conscious being. This is because a variable presupposes an object to which a value is assigned. Whether X causes Y depends on the choice of X, so causality is learned by learning X such that X causes Y, not by presupposing X and then learning if X causes Y (presupposing rather than inferring abstractions can reduce sample efficiency in learning). However, learning is a discriminatory process that requires states be differentiated by value. Without objects, variables or values, there is only quality. By this we mean an organism is attracted to or repulsed by a physical state. Learning reduces quality into objects by constructing policies classifying cause of affect (``representations'' are just behaviour triggered by phenomenal content). Where selection pressures require an organism classify its own interventions, that policy (a ``1st order causal identity'') has a quality that persists across interventions, and so there is something it is like to be that organism. Thus organisms have P-consciousness because it allows them to adapt with greater sample efficiency, and infer the cause of affect. We then argue neither P nor A-consciousness alone are remarkable, but when P-consciousness gives rise to A-consciousness we obtain ``H-consciousness'' (what Boltuc argues is the crux of the hard problem). This occurs when selection pressures require organism o infer organism u's prediction of o's interventions a ``2nd order causal identity'' approximating intent). A-consciousness is the contents of 2nd order causal identities, and by predicting another's prediction of one's own 1st order causal identities it becomes possible to know what one knows and feels, and act upon this information to communicate meaning in the Gricean sense. Thus P and A-consciousness are two aspects of H-concsiousness, the process of learning and acting in accord with a hierarchy of causal identities that simplify the environment into classifiers of cause and affect. We call this the psychophysical principle of causality.

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The brain is not mental! coupling neuronal and immune cellular processing in human organisms

Cited by 12 publications

References 168 publications

Collective intelligence: A unifying concept for integrating biology across scales and substrates

Collective intelligence: A unifying concept for integrating biology across scales and substrates

Universal Complexity Science and Theory of Everything: Challenges and Prospects

The Phenomenal Is Functional: A Unified Theory of Consciousness and Computation

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