Practopoiesis is a theory on how life organizes, including the organization of a mind. It proposes the principles by which adaptive systems function. One the same theory covers the life and the mind. It is a general theory of what it takes to be biologically intelligent. Being general, the theory is applicable to the brain as much as it is applicable to artificial intelligence (AI) technologies. What makes the theory so general is that it is grounded in the principles of cybernetics, rather than describing the physiological implementations of those mechanisms (inhibition/excitation, plasticity, etc.).
The most important presumption about the brain that practopoietic theory challanges is the generally accepted idea that the dynamics of a neural network (with its excitatory and inhibitory mechanisms) is sufficient to implement a mind. Practopoiesis tells us that this is not enough. Something is missing. Practopoiesis also offers answers on what is missing, both theoretically and in a form of a concrete implementation. The theoretical answer is in T3-systems and the processes of anapoiesis. The concrete implementation in the brain is based on the neural adaptation mechanisms. These mechanisms enables us to deal adaptively with the context within which we have to operate and thus, to be intelligent.
Nikola Danaylov, aka Socrates of Singularityweblog, interviewed me on practopoiesis:
In practopoiesis there is no longer a cycle: action->representation->action… . Instead, practopoietic theory works with actions only, representations being implicit. That way, practopoiesis operates non-symbolically. It turns out that, instead of producing symbols, execution of a mental operation results in creation of an action system. In other words, what occurs is poiesis of praxis (hence the name practopoiesis): Operations of one mechanism for acting create another mechanism for acting.
These action mechanisms form a hierarchy: One action system is in a service of another. The action hierarchy (or practopoietic hierarchy) begins with actions of gene expression mechanisms and ends with our overt behavior. Learning and cognitive functions are implemented as actions that operate at intermediate stages. The entire theory is founded in the cybernetic theorems of requisite variety and good regulator.
There are several advantages of grounding theories in an abstract, cybernetic way. One advantage is the applicability of the theory to system relying on different physical implementation e.g., brain vs. other organs, artificial intelligence algorithms, control systems, etc. Another advantage is the ability to detect what is missing in our current theories. In fact, an important contribution of practopoietic theory is the insight that our classical approach to organization of the brain — based on neural networks and the learning mechanisms for the connectivity of those networks — is not sufficient. One additional adaptive mechanism is needed and only then can we explain or mimic biological-like intelligence. The formal theory is presented in this paper downloadable from arXiv.org.
For those who need to strengthen their ability to think in terms of cybernetics, I can recommend the following review paper on cybernetic theory.
Practopoietic theory consists of two parts. The first part is the foundation. This is where the basic principles of adaptive systems are formulated. These principles can be applied to various biological processes, not only to the brain. Also, the first part can be applied to non-biological systems, such as AI. The second part applies those principles to human mind and to the mind/body problem. The second part explains the ways in which the mind is special and different from any other adaptive system.
The manuscript on practopoiesis discusses a number of implications for the mind-body problem.
Relation to biology
– Theory of evolution as a special case of practopoiesis
– Allostasis vs. homeostasis as a special case of practopoiesis
– Central dogma of molecular biology, a special case of knowledge shielding
AI and machine learning
– Limitations of information (and computation) paradigm, and why poiesis is better
– Origins of semantics and understanding (Chinese room problem)
– Formation and activation of concepts
– Abductive reasoning and learning priors for Bayesian inference
– Why our memory is reconstructive
– Where problem solving capabilities come from
– How does a practopoietic system become aware of the surrounding world?
– Why working memory and attention share resources
– Why our mind has automatic and controlled processes (System 1 and System 2)
– Limitations of Hebbian learning
– Limitations of computations by network
– Empirical predictions: The function of neural adaptation and its learning (revised version only)
Philosophy of mind
– Solution to the mystery of downward causation (revised version only)
– The nature of thought according to anapoietic theory (revised version only)
See also these implications.
First part, foundation: The principles of adaptive systems
-Requisite variety (Wikipedia)
-Good regulator theorem (The most important “forgotten” theorem; Wikipedia)
-Levels of adaptive organization
-Specificity vs. generality of cybernetic knowledge
-Environment-specific feedback (Eco-feedback)
-Downward pressure for adjustment
-Practopoietic loop of causation (animated)
Second part: Application to the brain/mind (and AI)
-Distinction between T2- and T3-systems
-Anapoietic (tri-traversal) theory of the mind
-Logical abduction (Wikipedia)
-Extended Wisconsin card-sorting test
-Chinese room argument (Wikipedia)
-Neural adaptation (Wikipedia)
Nikolić, D. (2014)
Practopoiesis: How cybernetics of biology can help AI