Mind as a Layered Network of Computational Processes all the Way Down to Quantum

Mind as a Layered Network of Computational Processes all the Way Down to Quantum

Gordana Dodig CrnkovicProfessor of Computer ScienceSchool of Innovation, Design and Engineering Mälardalen University, Chalmers & University of GothenburgSweden

http://www.idt.mdh.se/~gdc/

IACAP 14 Conference of the International Association for Computing and Philosophy

http://www.idt.mdh.se/~gdc/

Mälardalen University Chalmers & GU - Sweden

Mind as a layered network of computational processes all the way down to quantum

Response to a classical criticism against computational models of mind

● Classical criticism against computational models of mind is based on a model of computation as a discrete symbol manipulation of the Turing machine type.

● In this work I will present alternative computational model that avoids all the problems of old computationalism and meets Sprevak’s desiderata for a computational theory of mind.

● This is just a very broad first sketch of the naturalized computational approach that will need years to complete incorporating the results of new projects on brain research and new computational models such as DNN and others.

The plan of my talk

In this talk I will argue that mind can be seen as manifestation of a layered network of computational processes all the way down to quantum. The common framework through all layers is built on information (as a relational concept that defines structures for an agent) and computation that represents a dynamics of informational structure.

Mind as a highest level of information processing dissolves into cognition in a lower level that dissolves into metabolism on the lowest level of organization that dissolves into chemistry that dissolves in physics which ends with quantum level.

Levels are essential for understanding of mind as a structured complex process.


the Oxford dictionary definitionThe element of a person that enables them to be aware of the world and their experiences, to think, and to feel; the faculty of consciousness and thought: a lot of thoughts ran through my mindA person’s ability to think and reason; the intellect: his keen mindA person’s memory: the company’s name slips my mindA person identified with their intellectual faculties: he was one of the greatest minds of his time

As such kind of higher order process, mind relies on lower level processes that we can identify with cognition.Mind is set of processes in which consciousness, perception, affectivity, judgment, thinking, and will are based.

What is mind?


Mind: relationships with other concepts

http://www.visuwords.com Mind as a layered network of computational processes all the way down to quantum

http://www.visuwords.com/

After half a century of research in cognitive science, cognition still lacks a commonly accepted definition (Lyon, 2005).

Textbook description of cognition:“all the processes by which sensory input is transformed, reduced, elaborated, stored, recovered and used” (Neisser, 1967) is so broad that it includes present day robots.

On the other hand, the Oxford dictionary definition: “the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses” applies only to humans.

What is cognition?

*Mental = relating to the mind. Mind is set of processes in which consciousness, perception, affectivity, judgment, thinking, and will are based.


Cognition - relationships


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●Traditional anthropogenic approach to cognition* – only humans are cognitive agents●Biogenic approaches* – cognition is ability of all living organisms, no matter how “primitive” – goes a level below the complexity of human language – to complex systems chemical signaling and regulation processes. (Maturana & Varela, 1980; Maturana, 1970), argued that cognition and life are identical processes. ●New sub-biotic approaches to cognition assume that it is possible to construct cognitive agents starting from abiotic systems – a level below biogenic cognition. The question is if abiotic systems can be considered cognitive, in what sense and on which level.

Cognition at different levels of organization of living organism – from cells up

* (Lyon, 2005)


We focus on cognition and propose the common framework for understanding Anthropogenic, Biogenic and Abiotic Cognition.

We argue that (as in the rest of biology) – nothing makes sense except for in the light of evolution (Dobzhansky, 1973)and the cognition as a process can only be understood in the light of evolution.

Regarding abiotic systems we will compare their “cognitive behavior” with living organisms, and draw conclusions.

Connecting anthropogenic with biogenic and abiotic cognition


● “A cognitive system is a system whose organization defines a domain of interactions in which it can act with relevance to the maintenance of itself, and the process of cognition is the actual (inductive) acting or behaving in this domain. Living systems are cognitive systems, and living as a process is a process of cognition. This statement is valid for all organisms, with and without a nervous system.” (Maturana, 1970)

● In 1991, Kampis proposed a unified model of computation as the mechanism underlying biological processes through “self-generation of information by non-trivial change (self-modification) of systems” (Kampis, 1991. Self-Modifying Systems in Biology and Cognitive Science: A New Framework for Dynamics, Information and Complexity).

Living as a process is a process of cognition


Cognition as biological phenomenonStarting from anthropogenic perspective: The brain development

http://lcn.brain.riken.jp/tool_kit_evolution.htm

“The brain development may be carried out based on the basic body-organization-blueprints that are specific to an animal species depending on their strategy to survive in an environment.

To understand how our brains are established in the course of evolution, we have been conducting a comparison of the structure and function of the gene that are essential for establishing body organization and brain development in a wide rage of animals with nervous system.”


When we model higher levels of organization as generated by organization of lower levels, we acknowledge the importance of context for a process to develop a structure.

http://lcn.brain.riken.jp/tool_kit_evolution.htm

Digression: wonders of evolution – the smallest insect with brain, smaller than an amoeba

http://www.sciencedirect.com/science/article/pii/S1467803911000946 The smallest insects evolve anucleate neuronsArthropod Structure & Development, Volume 41, Issue 1, January 2012, Pages 29–34

Size of the smallest insect and two protozoans in comparison. (A)Megaphragma mymaripenne. (B)Paramecium caudatum. (C)Amoeba proteus.

Scale bar for A–C is 200 μm.

B and C are made up of a single cell, A the wasp complete with eyes, brain, wings, muscles, guts – is actually smaller.

This wasp is the third smallest insect alive.

the smallest nervous systems of any insect, consisting of just 7,400 neurons. Housefly has 340,000 Honeybee has 850,000. 95% of the wasps’s neurons have no nucleus.


http://www.sciencedirect.com/science/article/pii/S1467803911000946

Human brain studies

Human connectome http://outlook.wustl.edu/2013/jun/human-connectome-project

http://www.nature.com/scientificamerican/journal/v306/n6/pdf/scientificamerican0612-50.pdf The Human Brain Project

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Natural information processing

The Human Brain Project (HBP) is a large scientific research project, directed by the École Polytechnique Fédérale de Lausanne and largely funded by the European Union, which aims to simulate the complete human brain on supercomputers to better understand how it functions.

The BRAIN Initiative (Brain Research through Advancing Innovative Neurotechnologies, also referred to as the Brain Activity Map Project) is a proposed collaborative research initiative announced by the Obama administration on April 2, 2013, with the goal of mapping the activity of every neuron in the human brain. Based upon the Human Genome Project, the initiative has been projected to cost more than $300 million per year for ten years. (Wikipedia)

The Allen Institute large-scale brain mapping. In 2012 three additional major research initiatives: Neural Coding (understanding how information is encoded and decoded in the mammalian brain) Molecular Networks (understanding how information is encoded and decoded within a cell) Cell Types (large-scale descriptive resources of human and mouse brain cell types at molecular, morphological and connectional levels) Atlasing (collection of online public resources integrating extensive genomic and neuroanatomic data) http://www.alleninstitute.org/science/research_programs/index.html

Current brain research initiatives

● Symbolic modeling evolved from the computer science paradigms using the technologies of Knowledge-based systems - "Good Old-Fashioned Artificial Intelligence" (GOFAI). Used in expert systems and cognitive decision making, and extended to socio-cognitive approach.

● Subsymbolic modeling includes Connectionist/neural network models.

Current computational modeling in cognitive science

Current computational modeling in cognitive science

● Dynamical systems theory closely related to ideas about the embodiment of mind and the environmental situatedness of human cognition based on physiological and environmental events. The most important here is the dimension of time.

● Neural-symbolic integration techniques putting symbolic models and connectionist models into correspondence.

● Bayesian models of brain function which assume that the nervous system maintains internal probabilistic models that are updated by neural processing of sensory information using methods approximating those of Bayesian probability.

Information is defined as the difference in one physical system that makes the difference in another physical system.

This reflects the relational character of information and thus agent-dependency which calls for agent-based or actor models.

As a synthesis of informational structural realism and natural computationalism, I propose info-computational structuralism that builds on two basic concepts: information (as a structure) and computation (as a dynamics of an informational structure) (Dodig-Crnkovic, 2011).

(Dodig-Crnkovic & Giovagnoli, 2013) Information and computation are two basic and inseparable elements necessary for naturalizing <cognition>. (Dodig-Crnkovic, 2009)

Reality for an agent - informational structure with computational dynamics

“Information is the difference that makes a difference. “ Gregory Bateson It is the difference in the world that makes the difference for an agent. Here the world includes agents themselves too.

“Information expresses the fact that a system is in a certain configuration that is correlated to the configuration of another system. Any physical system may contain information about another physical system.” Carl Hewitt

Information as a fabric of reality for an agent (information is relational)

Bateson, G. (1972). Steps to an Ecology of Mind: Collected Essays in Anthropology, Psychiatry, Evolution, and Epistemology pp. 448–466). University Of Chicago Press.

Hewitt, C. (2007). What Is Commitment? Physical, Organizational, and Social. In P. Noriega, J. Vazquez, Salceda, G. Boella, O. Boissier, & V. Dign (Eds.), Coordination, Organizations, Institutions, and Norms in Agent Systems II (pp. 293 –307). Berlin, Heidelberg: Springer Verlag.

Informational structural realism (Floridi, Sayre) argues that information (for an agent) constitutes the fabric of reality:

Reality consists of informational structures organized on different levels of abstraction/resolution.

See also:

Van Benthem and Adriaans (2008) Philosophy of Information, In: Handbook of the philosophy of science series. http://www.illc.uva.nl/HPI

Ladyman J. and Ross D., with Spurrett D. and Collier J. (2007) Every Thing Must Go: Metaphysics Naturalized, Oxford UP

Information structures as a fabric of reality (thus structured/organized data) for an agent

Floridi, L. (2008) A defence of informational structural realism, Synthese 161, 219-253.Sayre, K. M. (1976) Cybernetics and the Philosophy of Mind, Routledge & Kegan Paul, London.

Combining definitions of Bateson:“ Information is a difference that makes a difference.” (Bateson, 1972) and Hewitt:”Information expresses the fact that a system is in a certain configuration that is correlated to the configuration of another system. Any physical system may contain information about another physical system.” (Hewitt, 2007), we get:

Information is defined as the difference in one physical system that makes the difference in another physical system.

The relational definition of information

For all living agents, information is the fabric of reality.

But: the knowledge of structures is only half a story. The other half are changes, processes – information dynamics. (In classical formulation: being and becoming.)

Information processing will be taken as the most general definition of computation.

This definition of computation has a profound consequence – if computation is the dynamics of informational structures of the universe, the dynamics of the universe is a network of computational processes (natural computationalism).

Structure vs. processInformation vs. computation

Dodig-Crnkovic, G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; Selected Papers from FIS 2010 Beijing, 2011.

It is important to notice:Computationalism is not what it used to be…

… that is, the thesis that persons are Turing machines.

Turing Machine is a model of computation equivalent to algorithm and it may be used for description of different processes in living organisms.

We need computational models for the basic characteristics of life as the ability to differentiate and synthesize information, make a choice, to adapt, evolve and learn in an unpredictable world. That requires computational mechanisms and models which are not mechanistic and predefined as Turing machine.*

* We need learning such as PAC Probably Approximately Correct – Leslie Valiant

Computationalism is not what it used to be …… that is the thesis that persons are Turing machines

Computational approaches that are capable of modelling adaptation, evolution and learning are found in the field of natural computation and computing nature.

Cognitive computing and cognitive robotics are the attempts to construct abiotic systems exhibiting cognitive characteristics.

It is argued that cognition comes in degrees, thus it is meaningful to talk about cognitive capabilities of artifacts, even though those are not meant to assure continuing existence, which was the evolutionary role of cognition in biotic systems.

“In the Actor Model [Hewitt, Bishop and Steiger 1973; Hewitt 2010], computation is conceived as distributed in space, where computational devices communicate asynchronously and the entire computation is not in any well-defined state.

(An Actor can have information about other Actors that it has received in a message about what it was like when the message was sent.) Turing's Model is a special case of the Actor Model.” (Hewitt, 2012)

Hewitt’s “computational devices” are conceived as computational agents – informational structures capable of acting on their own behalf.

Actor model of computation suitable to support the

relational concept of information - concurrent distributed

computation

Actors are the universal primitives of concurrent distributed digital computation. In response to a message that it receives, an Actor can make local <decisions>, create more Actors, send more messages, and designate how to respond to the next message received.

For Hewitt, Actors become Agents only when they are able to process expressions for commitments including the following: Contracts, Announcements, Beliefs, Goals, Intentions, Plans, Policies, Procedures, Requests, Queries.In other words, Hewitt’s Agents are human-like or if we broadly interpret the above capacities, life-like Actors.

Actor model of concurrent distributed computation

Computing nature and nature inspired computation

Peter J. Denning. 2007. Computing is a natural science.Commun. ACM 50, 7 (July 2007), 13-18. DOI=10.1145/1272516.1272529

http://doi.acm.org/10.1145/1272516.1272529

This image, by IBM scientists using an atomic force microscope, shows a nanographene molecule with carbon-carbon bond

Bacterial colony a multi-cellular “organism”Unicellular organism

DNA molecule

Atoms

Subatomic particles

Bacteria collectively “collects latent information from the environment and from other organisms, process the information, develop common knowledge, and thus learn from past experience” (Ben-Jacob, 2009)

http://www.ted.com/talks/bonnie_bassler_on_how_bacteria_communicate

Hierarchy of human body down to atoms

http://www.pc.maricopa.edu

http://www.cea1.com/anatomy-sistems/body-systems-2/

Levels of organization (interrelatedness)

1.Physical2.Chemical 3.Cellular 4.Tissue 5.Organ6.System 7.Organismal8.Social9.Ecological

Complex biological systems must be modeled as self-

referential, self-organizing "component-systems"

(George Kampis) which are self-generating and whose

behavior, though computational in a general sense, goes

far beyond Turing machine model.

“a component system is a computer which, when executing its operations

(software) builds a new hardware.... [W]e have a computer that re-wires itself in a

hardware-software interplay: the hardware defines the software and the software

defines new hardware. Then the circle starts again.” Kampis (1991) p. 223

Kampis (1991) Self-Modifying Systems in Biology and Cognitive Science. A New Framework For Dynamics, Information, and Complexity, Pergamon Press

Dodig Crnkovic, G. (2011). Significance of Models of Computation from Turing Model to Natural Computation. Minds and Machines, (R. Turner and A. Eden guest eds.) Volume 21, Issue 2, p.301.

Computing cells: self-generating systems

http://www.33rdsquare.com/2013/07/david-dalrymple-update-on-project.html

C. Elegans has 302 neurons (humans have 100 billion). The pattern of connections between neurons has been mapped out decades ago using electron microscopy, but knowledge of the connections is not sufficient to understand (or replicate) the information processor they represent, for some connections are inhibitory while others are excitatory.

Potential information - outside world for C-elegans

Cognition as interaction interface for C-elegans between potential information of the outside world and actual information of its inner world

Actual Information processing that creates reality for C-elegans

World as information for an agent

Reality for an agent is an informational structure with which agent interacts. As systems able to act on their own behalf and make sense (use) of information, cognitive agents are of special interest with respect to <knowledge>* generation.

This relates to the idea of participatory universe, (Wheeler, 1990) “it from bit” as well as to endophysics or “physics from within” where an observer is being within the universe, unlike the “god-eye-perspective” from the outside of the universe. (Rössler, 1998)

Reality for an agent – an observer-dependent reality

*<knowledge> for a very simple agent can be the ability to optimize gains and minimize risks.(Popper, 1999) p. 61 ascribes the ability to know to all living: ”Obviously, in the biological and evolutionary sense in which I speak of knowledge, not only animals and men have expectations and therefore (unconscious) knowledge, but also plants; and, indeed, all organisms.”

Info-computational framework and levels

The question of levels of organization/levels of abstraction for an agent is analyzed within the framework of info-computational constructivism, with natural phenomena modeled as computational processes on informational structures.

Info-computationalism is a synthesis of informational structuralism (nature is an informational structure for an agent) (Floridi, Sayre) and natural computationalism/pancomputationalism (nature computes its future states from its earlier states) (Zuse, Fredkin, Wolfram, Chaitin, Lloyd).

Two central books presenting the diversity of research on information and computation:

Adriaans P. and van Benthem J. eds. 2008. Philosophy of Information (Handbook of the Philosophy of Science) North Holland.

Rozenberg, G., T. Bäck, and J.N. Kok, eds. 2012. Handbook of Natural Computing. Berlin Heidelberg: Springer.

A living agent is an entity acting on its own behalf, with autopoietic properties that is capable of undergoing at least one thermodynamic work cycle. (Kauffman, 2000)

This definition differs from the common belief that (living) agency requires beliefs and desires, unless we ascribe some primitive form of <belief> and <desire> even to a very simple living agents such as bacteria. The fact is that they act on some kind of <anticipation> and according to some <preferences> which might be automatic in a sense that they directly derive from the organisms morphology. Even the simplest living beings act on their own behalf.

Living agents – basic levels of cognition

Although a detailed physical account of the agents capacity to perform work cycles and so persist* in the world is central for understanding of life/cognition, as (Kauffman, 2000) (Deacon, 2007) have argued in detail, present argument is primarily focused on the info-computational aspects of life.

Given that there is no information without physical implementation (Landauer, 1991), computation as the dynamics of information is the execution of physical laws.

*Contragrade processes (that require energy and do not spontaneously appear in nature) become possible by connecting with the orthograde (spontaneous) processes which provide source of energy.


Kauffman’s concept of agency (also adopted by Deacon) suggests the possibility that life can be derived from physics. That is not the same as to claim that life can be reduced to physics that is obviously false.

However, in deriving life from physics one may expect that both our understanding of life as well as physics will change.

We witness the emergence of information physics (Goyal, 2012) (Chiribella, G.; D’Ariano, G.M.; Perinotti, 2012) as a possible reformulation of physics that may bring physics and life/cognition closer to each other.


The origin of <cognition> in first living agents is not well researched, as the idea still prevails that only humans possess cognition and knowledge. However, there are different types of <cognition> and we have good reasons to ascribe simpler kinds of <cognition> to other living beings. Bacteria collectively “collects latent information from theenvironment and from other organisms, process the information, develop common knowledge, and thus learn from past experience” (Ben-Jacob, 2009)Plants can be said to possess memory (in their bodily structures) and ability to learn (adapt, change their morphology) and can be argued to possess simple forms of cognition.

Levels of organization of life/cognition

Ben-Jacob, E. (2009). Learning from Bacteria about Natural Information Processing. Annals of the New York Academy of Sciences, 1178, 78–90.

Evolution of the nervous system

Figure 9-1: Evolution of the nervous system

http://www.frontiersin.org/neuroscience/10.3389/fnins.2010.00200/full http://www.scienceprog.com/ecccerobot-embodied-cognition-in-a-compliantly-engineered-robot/

Information processing in the brain

Modular and hierarchically modular organization of brain networksD. Meunie, R. Lambiotte and E. T. BullmoreFrontiers of Neuroscience

http://www.neuroinformatics2013.orgNeuroinformatics

An example: developing cognitive computing at IBM

Design and construction of a brain-like computer

A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System Subrata Ghosh, Krishna Aswani, Surabhi Singh, Satyajit Sahu, Daisuke Fujita and Anirban Bandyopadhyay *

Information 2014, 5, 28-100; doi:10.3390/info5010028

http://www.mdpi.com/2078-2489/5/1/28

Connecting informational structures and processes from quantum physics to living organisms and societies

Nature is described as a complex informational structure for a cognizing agent.

Information is the difference in one information structure that makes a difference in another information structure.

Computation is information dynamics (information processing) constrained and governed by the laws of physics on the fundamental level.

Computing nature

The basic idea of computing nature is that all processes taking place in physical world can be described as computational processes – from the world of quantum mechanics to living organisms, their societies and ecologies. Emphasis is on regularities and typical behaviors.

Even though we all have our subjective reasons why we move and how we do that, from the bird-eye-view movements of inhabitants in a city show striking regularities.In order to understand big picture and behavior of societies, we take computational approach based on data and information.

See the work of Albert-László Barabási who studies networks on different scales:http://www.barabasilab.com/pubs-talks.php

We focus on cognition and propose the common framework for understanding anthropogenic, biogenic and abiotic cognition.

Cognition for biological system is synonymous with life. Cognition as a process can only be understood in the light of evolution.

Within the framework of info-computationalism, reality for an agent is an informational structure with computational dynamics. On different levels of organization, different kinds of cognition operate – from cellular level to organismic and social cognition.

Conclusion: Modeling Life as Cognitive Info-computation by Connecting

Anthropogenic with Biogenic and Abiotic Cognition

Ghosh, Subrata; Aswani, Krishna; Singh, Surabhi; Sahu, Satyajit; Fujita, Daisuke; Bandyopadhyay, Anirban. 2014. "Design and Construction of a Brain-Like Computer: A New Class of Frequency-Fractal Computing Using Wireless Communication in a Supramolecular Organic, Inorganic System.” Information 5, no. 1: 28-100. http://www.mdpi.com/2078-2489/5/1/28

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● Even though anthropogenic approach to cognition and mind is the oldest and by far the most dominant one, it is the most difficult approach to the most complex problem – embodied human brain.

● The study of biogenic and abiotic cognition can help us trace evolutionary roots of cognitive capacities in living organisms (biogenic) and construct (abiotic) artifacts with cognitive and intelligent behavior (cognitive computing and cognitive robotics).

● Therefore we start with simplest living systems such as bacteria to try to understand the basis of their cognitive behavior in informational structures and their dynamics (computational processes). At the same time brain studies provide us with new understanding of human brain and its function (mind).

Summary. The strategy of info-computational approach to cognition & mind

Info-computational approach is based on generalized concepts of information and computation. Short summary of the argument:

●<Information> presents a structure consisting of differences in one system that cause the differences in another system. In other words, information is <observer>*-relative. Information is organized/structured data for an agent.

●<Computation> is information processing (dynamics of information). It is physical process of morphological change in the informational structure (physical implementation of information, as there is no information without physical implementation.)

Information, computation, cognition. Hierarchies of levels based on agency

*<> brackets indicate that the term is used in a broader sense than usually.

3. Both <information> and <computation> appear on many different levels of organisation/abstraction/resolution/granularity of matter/energy in space/time.

4. Of all agents (entities capable of acting on their own behalf) only living agents have the ability to actively make choices so to increase the probability of their own continuing existence. This ability of living agents to act autonomously on its own behalf is based on the use of energy/matter and information from the environment.



5. <Cognition> consists of all (info-computational) processes necessary to keep living agent’s organizational integrity on all different levels of its existence. <Cognition> = <info-computation>

6. <Cognition> is equivalent with the (process of) life.* Its complexity increases with evolution.This complexification is a result of morphological computation.

7. <Mind> is a result of a succession of info-computational processes from quantum level up.

* Maturana, H. & Varela, F., 1980. Autopoiesis and cognition: the realization of the living, Dordrecht Holland: D. Reidel Pub. Co.


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Two brand new books

2014

On the topic of on the topic of (physical) computation & cognition.Written by a philosopher.

New computational paradigm:Generative computing – cellular automata

A New Kind of Science

Book available at:http://www.wolframscience.com

Based on cellular automata, complexity emerging from repeating very simple rules

See alsohttp://www.youtube.com/watch?v=_eC14GonZnU

A New Kind of Science - Stephen Wolfram

Books in the New Computational Paradigm

A New Paradigm of Computing – Interactive Computing

Interactive Computation: the New Paradigm Springer-Verlag in September 2006

Dina Goldin, Scott Smolka, Peter Wegner, eds.

Dina Goldin, Peter Wegner The Interactive Nature of Computing: Refuting the Strong Church - Turing Thesis Minds and MachinesVolume 18 , Issue 1 (March 2008) p 17 - 38 http://www.cs.brown.edu/people/pw/strong-cct.pdf

Biology as Reactivity http://research.microsoft.com/pubs/144550/CACM_11.pdf

Self-modifying Systems in Biology and Cognitive Science

The topic of the book is the self-generation of

information by the self-modification of

systems. The author explains why biological

and cognitive processes exhibit identity

changes in the mathematical and logical

sense. This concept is the basis of a new

organizational principle which utilizes shifts of

the internal semantic relations in systems. ftp://wwwc3.lanl.gov/pub/users/joslyn/kamp_rev.pdf

Programming the Universe: A Quantum Computer Scientist Takes on the Cosmos

by Seth Lloyd

The Universe as quantum information

The Universe as quantum information

Decoding Reality

By Valtko Vedral

Reality = Information

Under Google books there are parts of this book available.

Self-Organization and Selection in Evolution

Stuart Kauffman presents a brilliant new paradigm for evolutionary biology, one that extends the basic concepts of Darwinian evolution to accommodate recent findings and perspectives from the fields of biology, physics, chemistry and mathematics. The book drives to the heart of the exciting debate on the origins of life and maintenance of order in complex biological systems.

It focuses on the concept of self-organization: the spontaneous emergence of order widely observed throughout nature. Kauffman here argues that self-organization plays an important role in the emergence of life itself and may play as fundamental a role in shaping life's subsequent evolution as does the Darwinian process of natural selection.

http://books.google.se/books/about/The_Origins_of_Order.html?id=lZcSpRJz0dgC&redir_esc=y

The relationship between mind and matter

Incomplete Nature. How mind emerged

from matter

by Terrence Deacon

“We can only see a short distance ahead, but we can see plenty there that needs to be done.”

(Turing 1950)

Turing, A. M. (1950). Computing machinery and intelligence, Mind LIX, 433-60. http://cogprints.org/499/0/turing.html

Let me finish by Turing quote

Based on the following articles

● Dodig-Crnkovic G. and Giovagnoli R. (Eds), Computing Nature – A Network of Networks

of Concurrent Information Processes, In: COMPUTING NATURE, (book) Springer, Heidelberg, SAPERE book series, pp. 1-22, May 2013. http://arxiv.org/abs/1210.7784

● Dodig-Crnkovic G., Dynamics of Information as Natural Computation, Information 2011, 2(3), 460-477; doi:10.3390/info2030460 Special issue: Selected Papers from FIS 2010 Beijing Conference, 2011. http://www.mdpi.com/journal/information/special_issues/selectedpap_beijing http://www.mdpi.com/2078-2489/2/3/460/ See also: http://livingbooksaboutlife.org/books/Energy_Connections

● Dodig Crnkovic, G. Information and Energy/Matter. Information 2012, 3(4), 751-755. Special Issue "Information and Energy/Matter" doi:10.3390/info3040751

All articles can be found under:

http://www.idt.mdh.se/~gdc/work/publications.html

THANKS TO

Prof. Francisco Hernández Quiroz for invitation to UNAM

Alberto Hernández Espinosa for organizing my visit to Infotec

Also

Hector ZenilCarlos Gershenson & Tom Froesefor their work that convinced me that UNAM is an extraordinary place!

A Mathematical Model for Info-

computationalism- A. C. Ehresmann

Open peer commentary on the article “Info-computational Constructivism and Cognition” by Gordana Dodig-Crnkovic.

Ehresmann proposes a mathematical approach to the framework developed by Dodig-Crnkovic. Based on the Property of natural computation, called the multiplicity principle development of increasingly complex cognitive processes and knowledge is described. “Local dynamics are classically computable, a consequence of the MP is that the global dynamics is not, thus raising the problem of developing more elaborate computation models.”

An Info-Computational Model for (Neuro-)cognitive Systems Capable of Creativity - Andrée C. Ehresmann

Andrée C. Ehresmann http://www.mdpi.com/1099-4300/14/9/1703

The model, based on a ‘dynamic’ Category Theory, accounting for the functioning of the neural, cognitive and mental systems at different levels of description and across different timescales.

Mind as a Layered Network of Computational Processes all the Way Down to Quantum

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