Showing posts with label Neurons. Show all posts
Showing posts with label Neurons. Show all posts

Thursday, May 31, 2012

Mirror Neurons

Neuroscientific evidence suggests that one basic entry point into understanding others' goals and feelings is the process of actively simulating in our own brain the actions we observe in others. This involves the firing of neurons that would be activated were we actually performing an action, although we are only observing it in someone else. Neurons performing mirroring functions have been directly observed in primates and other species, including birds. In humans, brain activity consistent with "mirroring" has been found in the premotor cortex, the supplementary motor area, the primary somatosensory cortex and the inferior parietal cortex.
The data revealed that even the most complex, abstract emotions—those that require maturity, reflection, and world knowledge to appreciate—do involve our most advanced brain networks. However, they seem to get their punch—their motivational push—from activating basic biological regulatory structures in the most primitive parts of the brain, those responsible for monitoring functions like heart rate and breathing. In turn, the basic bodily changes induced during even the most complex emotions—e.g., our racing heart or clenched gut—are "felt" by sensory brain networks. When we talk of having a gut feeling that some action is right or wrong, we are not just speaking metaphorically.

So, I'm saying the mirror neuron system underlies the interface allowing you to rethink about issues like consciousness,representation of self,what separates you from other human beings,what allows you to empathize with other human beings,and also even things like the emergence of culture and civilization,which is unique to human beings. See: VS Ramachandran: The neurons that shaped civilization 

  How important is the environment in that we might see the development of the conditions of "specific types of neurons" when the color can dictate the type of neuron developed? Can we say that the color(emotion) is an emotive state that we might indeed create in the type of consciousness with which we meet the world. A consciousness that that sets the trains of thought given the reality of our own perceptions. Or,  perpetuated thought processes unravelled in a world of our own illusions?

In a nutshell, what Karim showed was that each time a memory is used, it has to be restored as a new memory in order to be accessible later. The old memory is either not there or is inaccessible. In short, your memory about something is only as good as your last memory about it. Joseph LeDoux

Psychology professor Karim Nader is helping sufferers of post-traumatic stress disorder lessen debilitating symptoms—and in some cases, regain a normal life.Owen Egan See also: The Trauma Tamer See Also: Brain Storming

IC: Why is this research so important?

Karim Nader: There are a lot of implications. All psychopathological disorders, such as PTSD, epilepsy, obsessive compulsive disorders, or addiction—all these things have to do with your brain getting rewired in a way that is malfunctioning. Theoretically, we may be able to treat a lot of these psychopathologies. If you could block the re-storage of the circuit that causes the obsessive compulsion, then you might be able to reset a person to a level where they aren’t so obsessive. Or perhaps you can reset the circuit that has undergone epilepsy repeatedly so that you can increase the threshold for seizures. And there is some killer data showing that it’s possible to block the reconsolidation of drug cravings.

The other reason why I think it is so striking is that it is so contrary to what has been the accepted view of memory for so long in the mainstream. My research caused everybody in the field to stop, turn around and go, “Whoa, where’d that come from?” Nobody’s really working on this issue, and the only reason I came up with this is because I wasn’t trained in memory. [Nader was originally researching fear.] It really caused a fundamental reconceptualization of a very basic and dogmatic field in neuroscience, which is very exciting. It is the first time in 100 years that people are starting to come up with new models of memory at the physiological level.

Part of the understanding for me is that in creating this environment for neural development the retention of memory has to have some emotive basis which arises from the ancient part of our brain in that it is associated with the heart response.

 Savas Dimopoulos

Here’s an analogy to understand this: imagine that our universe is a two-dimensional pool table, which you look down on from the third spatial dimension. When the billiard balls collide on the table, they scatter into new trajectories across the surface. But we also hear the click of sound as they impact: that’s collision energy being radiated into a third dimension above and beyond the surface. In this picture, the billiard balls are like protons and neutrons, and the sound wave behaves like the graviton. See: The Sound Of Billiard Balls
While these physiological processes are going on in our bodies the chemical responses of emotion trigger manifestations in the world outside of our bodies. Let us say consciousness exists "at the periphery of our bodies." What measure then to assess the realization that such manifestations internally are in the control of our manipulations of living experience? Are we then not caught in the throes of and are we not  machine like to think such associations could have ever been produced in a robot like being manufactured?

Of course this is a fictional representation above of what may resound within and according to the experiences we may have? The question is then how are memories retained? How do memories transmit through out our endocrinology system the nature of our experiences so that we see consciousness as a form of the expression through which we color our world?

Monday, October 31, 2011

Wednesday, July 20, 2011

Igniting Neurons:Time Travel

Life must be understood backwards; but... it must be lived forward.
Soren Kierkegaard

The image illustrates the Wayback machine from the Mr. Peabody and Sherman segment of the Rocky and Bullwinkle cartoon. The image supports the article on the subject: Wayback machine. The screen shot was selected to illustrate the nature and size of the Wayback machine (compare to images of UNIVAC or ENIAC machines).

I mean whats sets the whole package off to wonder how such neurons once isolated,  as to being components of all the things we learn, then becomes a method by which we now see ? What sets off the spark to think that technologies will be superseded by the efforts by mind,  to think that all we have to do is turn the switch off? The technologies no longer work? That this is somehow the fate of a mind who no longer seeks to find meaning, or,  is settled to the fate of mundane happenings which replay them-self time and time again.

Boids is an artificial life program, developed by Craig Reynolds in 1986, which simulates the flocking behaviour of birds. His paper on this topic was published in 1987 in the proceedings of the ACM SIGGRAPH conference. The name refers to a "bird-like object", but its pronunciation evokes that of "bird" in a stereotypical New York accent.
As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. The rules applied in the simplest Boids world are as follows:

So, you've built up this vast reservoir of information as neurons, and all time that began from embryonic growth seeks to find them-self distinct to all the functions of the human body.  To think we have become who we are today,  as a sign of all these possibilities are but the evolution of a pattern played out as an example of the evolution of being manifested through this body? Manifest now,  once one expresses through the fingers as an extension of mind, to be built up, as all those things which represent self .

So you step back then, looking as if from outside, looking in,  as to wonder what is new being garnered are but piecemeal represents some larger view of the reality of groups, to present an awareness greater then that which is though to exist, as some local issue in it's understanding,  is more the societal flock with purpose, unawares of the significance of choices made? How do societies change?

Andrey Kravtsov's computer modelling comes to mind. See: Early Universe Formation

So there is then this reservoir of information, many facets and capabilities of mind to choose those things which are brought together through the journey,  all encompassing it's growth, this potential exists as if a flash, like lightning strikes from which are born new neuronal pathways. Perception, is then changed. Many connections in life take place where none were seen before.

See Also:

Saturday, January 22, 2011

The Edge World Question Center:

Evidently, something powerful had happened in my brain.FRANK WILCZEK

So this year.....


The term 'scientific"is to be understood in a broad sense as the most reliable way of gaining knowledge about anything, whether it be the human spirit, the role of great people in history, or the structure of DNA. A "scientific concept" may come from philosophy, logic, economics, jurisprudence, or other analytic enterprises, as long as it is a rigorous conceptual tool that may be summed up succinctly (or "in a phrase") but has broad application to understanding the world. The Edge Question 2011



Physicist, MIT; Recipient, 2004 Nobel Prize in Physics; Author, The Lightness of Being

In this picture, the flow of information runs from top to bottom. Sensory neurons — the eyeballs at the top — take input from the external world and encode it into a convenient form (which is typically electrical pulse trains for biological neurons, and numerical data for the computer "neurons" of artificial neural networks). They distribute this encoded information to other neurons, in the next layer below. Effector neurons — the stars at the bottom — send their signals to output devices (which are typically muscles for biological neurons, and computer terminals for artificial neurons). In between are neurons that neither see nor act upon the outside world directly. These inter-neurons communicate only with other neurons. They are the hidden layers.WHAT SCIENTIFIC CONCEPT WOULD IMPROVE EVERYBODY'S COGNITIVE TOOLKIT?



Can we say that no two pathways are ever the same, then to what value "applying constraints on communication, then are they not the idea of limiting the potential, through connection of "other hidden layer neurons," in other people, are also activated? What is "the link" between this communication?

....finally, having the ability to dream:) See:Creating the Perfect Human Being or Maybe.....

Expository on one level to realize that such diversity could create in the person the ability to dream, and actualize image creation according to the information at hand, how illusionist indeed that "such shapes" can come out of such communications within that that hidden layer? How would such application be realized according to computerized development?


We had an old headboard, which was so rickety that it had to be leaned against a wall. This requirement was a constraint on the positioning of the headboard. The other pieces of furniture also had requirements (constraints) on where they could be placed.Constraint Satisfaction

How "large" the point/synapse, how large the room, how the large the universe? It's just a word. But it's culpability of "dimensional significance" is the ultimate picture that comes out of the resulting fabrication from the hidden layers? Just as one might think of string theory and the real world?

Tuesday, November 23, 2010

The Synapse of the Wondering Mind

Click here for Penrose's Seminar

While trying to organize my thoughts about the title of this blog entry, it becomes apparent to me that the potential of neurological transposition of electrical pulses is part of the function of the physical system in order to operate, while I am thinking something much different.

It is the idea of our being receptive too something more then a signal transfer within the physical system of pathways established through repetitive use, but also the finding of that location, to receive.It is one where we can accept something into ourselves as information from another. As accepting information from around us. Information is energy?


Structure of a typical chemical synapse
In the nervous system, a synapse is a junction that permits a neuron to pass an electrical or chemical signal to another cell. The word "synapse" comes from "synaptein", which Sir Charles Scott Sherrington and colleagues coined from the Greek "syn-" ("together") and "haptein" ("to clasp").

Synapses are essential to neuronal function: neurons are cells that are specialized to pass signals to individual target cells, and synapses are the means by which they do so. At a synapse, the plasma membrane of the signal-passing neuron (the presynaptic neuron) comes into close apposition with the membrane of the target (postsynaptic) cell. Both the presynaptic and postsynaptic sites contain extensive arrays of molecular machinery that link the two membranes together and carry out the signaling process. In many synapses, the presynaptic part is located on an axon, but some presynaptic sites are located on a dendrite or soma.
There are two fundamentally different types of synapse:
  • In a chemical synapse, the presynaptic neuron releases a chemical called a neurotransmitter that binds to receptors located in the postsynaptic cell, usually embedded in the plasma membrane. Binding of the neurotransmitter to a receptor can affect the postsynaptic cell in a wide variety of ways.
  • In an electrical synapse, the presynaptic and postsynaptic cell membranes are connected by channels that are capable of passing electrical current, causing voltage changes in the presynaptic cell to induce voltage changes in the postsynaptic cell.


The Einstein-Podolsky-Rosen Argument in Quantum Theory

First published Mon May 10, 2004; substantive revision Wed Aug 5, 2009

In the May 15, 1935 issue of Physical Review Albert Einstein co-authored a paper with his two postdoctoral research associates at the Institute for Advanced Study, Boris Podolsky and Nathan Rosen. The article was entitled “Can Quantum Mechanical Description of Physical Reality Be Considered Complete?” (Einstein et al. 1935). Generally referred to as “EPR”, this paper quickly became a centerpiece in the debate over the interpretation of the quantum theory, a debate that continues today. The paper features a striking case where two quantum systems interact in such a way as to link both their spatial coordinates in a certain direction and also their linear momenta (in the same direction). As a result of this “entanglement”, determining either position or momentum for one system would fix (respectively) the position or the momentum of the other. EPR use this case to argue that one cannot maintain both an intuitive condition of local action and the completeness of the quantum description by means of the wave function. This entry describes the argument of that 1935 paper, considers several different versions and reactions, and explores the ongoing significance of the issues they raise. See Also:Historical Figures Lead Us to the Topic of Entanglement
When looking at Penrose's seminar and you have clicked on the image, the idea presented itself to me that if one was to seek "a method by determination" I might express color of gravity as a exchange in principle as if spooky action at a distance, as an expression of a representative example of colorimetric expressions.

Science and TA by Chris Boyd
Do we selectively ignore other models from artificial intelligence such as Zadeh's Fuzzy Logic? This is a logic used to model perception and used in newly designed "smart" cameras. Where standard logic must give a true or false value to every proposition, fuzzy logic assigns a certainty value between zero and one to each of the propositions, so that we say a statement is .7 true and .3 false. Is this theory selectively ignored to support our theories?

Here fuzzy logic and TA had served in principal to show orders between "O and 1" as potentials of connection between the source of exchange between those two individuals. I see "cryptography" as an example of this determination  as a defined state of reductionism through that exchange.

Stuart Kauffman raises his own philosophical ideas in "Beyond Einstein and Schrodinger? The Quantum Mechanics of Closed Quantum Systems" about such things,  that lead to further  ideas on his topic, has blocked my comments there, so I see no use in further participating and offering ideas for his efforts toward "data mining" with regard to his biological methods to determination.

I can say it has sparked further interest in my own assessment of "seeking to understand color of gravity" as a method to determination,  as a state of deduction orientation, that we might get from a self evidential result from exchange,  as a "cause of determination" as to our futures.

While I have listed here between two individuals these thoughts also act as "an antennae" toward a universal question of "what one asks shall in some form be answered."

Not just a "blank slate" but one with something written on it. What design then predates physical expression, as if one could now define the human spirit and character, as  the soul in constant expression through materiality? An "evolution of spirit" then making manifest our progressions, as leading from one position to another.

See Also:

The Synapse is a Portal of the Thinking Mind

Monday, July 05, 2010



From Wikipedia, the free encyclopedia

Self-organization is the process where a structure or pattern appears in a system without a central authority or external element imposing it. This globally coherent pattern appears from the local interaction of the elements that makes up the system, thus the organization is achieved in a way that is parallel (all the elements act at the same time) and distributed (no element is a coordinator).



The most robust and unambiguous examples[1] of self-organizing systems are from the physics of non-equilibrium processes. Self-organization is also relevant in chemistry, where it has often been taken as being synonymous with self-assembly. The concept of self-organization is central to the description of biological systems, from the subcellular to the ecosystem level. There are also cited examples of "self-organizing" behaviour found in the literature of many other disciplines, both in the natural sciences and the social sciences such as economics or anthropology. Self-organization has also been observed in mathematical systems such as cellular automata.
Sometimes the notion of self-organization is conflated with that of the related concept of emergence.[citation needed] Properly defined, however, there may be instances of self-organization without emergence and emergence without self-organization, and it is clear from the literature that the phenomena are not the same. The link between emergence and self-organization remains an active research question.
Self-organization usually relies on four basic ingredients [2]:
  1. Strong dynamical non-linearity, often though not necessarily involving Positive feedback and Negative feedback
  2. Balance of exploitation and exploration
  3. Multiple interactions

History of the idea

The idea that the dynamics of a system can tend by themselves to increase the inherent order of a system has a long history. One of the earliest statements of this idea was by the philosopher Descartes, in the fifth part of his Discourse on Method, where he presents it hypothetically.[citation needed] Descartes further elaborated on the idea at great length in his unpublished work The World.
The ancient atomists (among others) believed that a designing intelligence was unnecessary, arguing that given enough time and space and matter, organization was ultimately inevitable, although there would be no preferred tendency for this to happen. What Descartes introduced was the idea that the ordinary laws of nature tend to produce organization [citation needed] (For related history, see Aram Vartanian, Diderot and Descartes).
Beginning with the 18th century naturalists, a movement arose that sought to understand the "universal laws of form" in order to explain the observed forms of living organisms. Because of its association with Lamarckism, their ideas fell into disrepute until the early 20th century, when pioneers such as D'Arcy Wentworth Thompson revived them. The modern understanding is that there are indeed universal laws (arising from fundamental physics and chemistry) that govern growth and form in biological systems.
Originally, the term "self-organizing" was used by Immanuel Kant in his Critique of Judgment, where he argued that teleology is a meaningful concept only if there exists such an entity whose parts or "organs" are simultaneously ends and means. Such a system of organs must be able to behave as if it has a mind of its own, that is, it is capable of governing itself.
In such a natural product as this every part is thought as owing its presence to the agency of all the remaining parts, and also as existing for the sake of the others and of the whole, that is as an instrument, or organ... The part must be an organ producing the other parts—each, consequently, reciprocally producing the others... Only under these conditions and upon these terms can such a product be an organized and self-organized being, and, as such, be called a physical end.
The term "self-organizing" was introduced to contemporary science in 1947 by the psychiatrist and engineer W. Ross Ashby[3]. It was taken up by the cyberneticians Heinz von Foerster, Gordon Pask, Stafford Beer and Norbert Wiener himself in the second edition of his "Cybernetics: or Control and Communication in the Animal and the Machine" (MIT Press 1961).
Self-organization as a word and concept was used by those associated with general systems theory in the 1960s, but did not become commonplace in the scientific literature until its adoption by physicists and researchers in the field of complex systems in the 1970s and 1980s.[4] After 1977's Ilya Prigogine Nobel Prize, the thermodynamic concept of self-organization received some attention of the public, and scientific researchers start to migrate from the cybernetic view to the thermodynamic view.


The following list summarizes and classifies the instances of self-organization found in different disciplines. As the list grows, it becomes increasingly difficult to determine whether these phenomena are all fundamentally the same process, or the same label applied to several different processes. Self-organization, despite its intuitive simplicity as a concept, has proven notoriously difficult to define and pin down formally or mathematically, and it is entirely possible that any precise definition might not include all the phenomena to which the label has been applied.
It should also be noted that, the farther a phenomenon is removed from physics, the more controversial the idea of self-organization as understood by physicists becomes. Also, even when self-organization is clearly present, attempts at explaining it through physics or statistics are usually criticized as reductionistic.
Similarly, when ideas about self-organization originate in, say, biology or social science, the farther one tries to take the concept into chemistry, physics or mathematics, the more resistance is encountered, usually on the grounds that it implies direction in fundamental physical processes. However the tendency of hot bodies to get cold (see Thermodynamics) and by Le Chatelier's Principle- the statistical mechanics extension of Newton's Third Law- to oppose this tendency should be noted.

Self-organization in physics

Convection cells in a gravity field
There are several broad classes of physical processes that can be described as self-organization. Such examples from physics include:
  • self-organizing dynamical systems: complex systems made up of small, simple units connected to each other usually exhibit self-organization

  • In spin foam system and loop quantum gravity that was proposed by Lee Smolin. The main idea is that the evolution of space in time should be robust in general. Any fine-tuning of cosmological parameters weaken the independency of the fundamental theory. Philosophically, it can be assumed that in the early time, there has not been any agent to tune the cosmological parameters. Smolin and his colleagues in a series of works show that, based on the loop quantization of spacetime, in the very early time, a simple evolutionary model (similar to the sand pile model) behaves as a power law distribution on both the size and area of avalanche.

    • Although, this model, which is restricted only on the frozen spin networks, exhibits a non-stationary expansion of the universe. However, it is the first serious attempt toward the final ambitious goal of determining the cosmic expansion and inflation based on a self-organized criticality theory in which the parameters are not tuned, but instead are determined from within the complex system.[5]

Self-organization vs. entropy

Statistical mechanics informs us that large scale phenomena can be viewed as a large system of small interacting particles, whose processes are assumed consistent with well established mechanical laws such as entropy, i.e., equilibrium thermodynamics. However, “… following the macroscopic point of view the same physical media can be thought of as continua whose properties of evolution are given by phenomenological laws between directly measurable quantities on our scale, such as, for example, the pressure, the temperature, or the concentrations of the different components of the media. The macroscopic perspective is of interest because of its greater simplicity of formalism and because it is often the only view practicable.” Against this background, Glansdorff and Ilya Prigogine introduced a deeper view at the microscopic level, where “… the principles of thermodynamics explicitly make apparent the concept of irreversibility and along with it the concept of dissipation and temporal orientation which were ignored by classical (or quantum) dynamics, where the time appears as a simple parameter and the trajectories are entirely reversible.”[6]
As a result, processes considered part of thermodynamically open systems, such as biological processes that are constantly receiving, transforming and dissipating chemical energy (and even the earth itself which is constantly receiving and dissipating solar energy), can and do exhibit properties of self organization far from thermodynamic equilibrium.
A laser can also be characterized as a self organized system to the extent that normal states of thermal equilibrium characterized by electromagnetic energy absorption are stimulated out of equilibrium in a reverse of the absorption process. “If the matter can be forced out of thermal equilibrium to a sufficient degree, so that the upper state has a higher population than the lower state (population inversion), then more stimulated emission than absorption occurs, leading to coherent growth (amplification or gain) of the electromagnetic wave at the transition frequency.”[7]

Self-organization in chemistry

The DNA structure at left (schematic shown) will self-assemble into the structure visualized by atomic force microscopy at right. Image from Strong.[8]
Self-organization in chemistry includes:
  1. molecular self-assembly
  2. reaction-diffusion systems and oscillating chemical reactions
  3. autocatalytic networks (see: autocatalytic set)
  4. liquid crystals
  5. colloidal crystals
  6. self-assembled monolayers
  7. micelles
  8. microphase separation of block copolymers
  9. Langmuir-Blodgett films

Self-organization in biology

Birds flocking, an example of self-organization in biology
According to Scott Camazine.. [et al.]:
In biological systems self-organization is a process in which pattern at the global level of a system emerges solely from numerous interactions among the lower-level components of the system. Moreover, the rules specifying interactions among the system's components are executed using only local information, without reference to the global pattern.[9]
The following is an incomplete list of the diverse phenomena which have been described as self-organizing in biology.
  1. spontaneous folding of proteins and other biomacromolecules
  2. formation of lipid bilayer membranes
  3. homeostasis (the self-maintaining nature of systems from the cell to the whole organism)
  4. pattern formation and morphogenesis, or how the living organism develops and grows. See also embryology.
  5. the coordination of human movement, e.g. seminal studies of bimanual coordination by Kelso
  6. the creation of structures by social animals, such as social insects (bees, ants, termites), and many mammals
  7. flocking behaviour (such as the formation of flocks by birds, schools of fish, etc.)
  8. the origin of life itself from self-organizing chemical systems, in the theories of hypercycles and autocatalytic networks
  9. the organization of Earth's biosphere in a way that is broadly conducive to life (according to the controversial Gaia hypothesis)

Self-organization in mathematics and computer science

Gosper's Glider Gun creating "gliders" in the cellular automaton Conway's Game of Life.[10]
As mentioned above, phenomena from mathematics and computer science such as cellular automata, random graphs, and some instances of evolutionary computation and artificial life exhibit features of self-organization. In swarm robotics, self-organization is used to produce emergent behavior. In particular the theory of random graphs has been used as a justification for self-organization as a general principle of complex systems. In the field of multi-agent systems, understanding how to engineer systems that are capable of presenting self-organized behavior is a very active research area.

Self-organization in cybernetics

Wiener regarded the automatic serial identification of a black box and its subsequent reproduction as sufficient to meet the condition of self-organization.[11] The importance of phase locking or the "attraction of frequencies", as he called it, is discussed in the 2nd edition of his "Cybernetics".[12] Drexler sees self-replication as a key step in nano and universal assembly.
By contrast, the four concurrently connected galvanometers of W. Ross Ashby's Homeostat hunt, when perturbed, to converge on one of many possible stable states.[13] Ashby used his state counting measure of variety[14] to describe stable states and produced the "Good Regulator"[15] theorem which requires internal models for self-organized endurance and stability.
Warren McCulloch proposed "Redundancy of Potential Command"[16] as characteristic of the organization of the brain and human nervous system and the necessary condition for self-organization.
Heinz von Foerster proposed Redundancy, R = 1- H/Hmax , where H is entropy.[17] In essence this states that unused potential communication bandwidth is a measure of self-organization.
In the 1970s Stafford Beer considered this condition as necessary for autonomy which identifies self-organization in persisting and living systems. Using Variety analyses he applied his neurophysiologically derived recursive Viable System Model to management. It consists of five parts: the monitoring of performance[18] of the survival processes (1), their management by recursive application of regulation (2), homeostatic operational control (3) and development (4) which produce maintenance of identity (5) under environmental perturbation. Focus is prioritized by an "algedonic loop" feedback:[19] a sensitivity to both pain and pleasure.
In the 1990s Gordon Pask pointed out von Foerster's H and Hmax were not independent and interacted via countably infinite recursive concurrent spin processes[20] (he favoured the Bohm interpretation) which he called concepts (liberally defined in any medium, "productive and, incidentally reproductive"). His strict definition of concept "a procedure to bring about a relation"[21] permitted his theorem "Like concepts repel, unlike concepts attract"[22] to state a general spin based Principle of Self-organization. His edict, an exclusion principle, "There are No Doppelgangers"[23] means no two concepts can be the same (all interactions occur with different perspectives making time incommensurable for actors). This means, after sufficient duration as differences assert, all concepts will attract and coalesce as pink noise and entropy increases (and see Big Crunch, self-organized criticality). The theory is applicable to all organizationally closed or homeostatic processes that produce endurance and coherence (also in the sense of Reshcher Coherence Theory of Truth with the proviso that the sets and their members exert repulsive forces at their boundaries) through interactions: evolving, learning and adapting.
Pask's Interactions of actors "hard carapace" model is reflected in some of the ideas of emergence and coherence. It requires a knot emergence topology that produces radiation during interaction with a unit cell that has a prismatic tensegrity structure. Laughlin's contribution to emergence reflects some of these constraints.

Self-organization in human society

Social self-organization in international drug routes
The self-organizing behaviour of social animals and the self-organization of simple mathematical structures both suggest that self-organization should be expected in human society. Tell-tale signs of self-organization are usually statistical properties shared with self-organizing physical systems (see Zipf's law, power law, Pareto principle). Examples such as Critical mass (sociodynamics), herd behaviour, groupthink and others, abound in sociology, economics, behavioral finance and anthropology.[24]
In social theory the concept of self-referentiality has been introduced as a sociological application of self-organization theory by Niklas Luhmann (1984). For Luhmann the elements of a social system are self-producing communications, i.e. a communication produces further communications and hence a social system can reproduce itself as long as there is dynamic communication. For Luhmann human beings are sensors in the environment of the system.{p410 Social System 1995} Luhmann developed an evolutionary theory of Society and its subsytems, using functional analyses and systems theory. {Social Systems 1995}.
Self-organization in human and computer networks can give rise to a decentralized, distributed, self-healing system, protecting the security of the actors in the network by limiting the scope of knowledge of the entire system held by each individual actor. The Underground Railroad is a good example of this sort of network. The networks that arise from drug trafficking exhibit similar self-organizing properties. Parallel examples exist in the world of privacy-preserving computer networks such as Tor. In each case, the network as a whole exhibits distinctive synergistic behavior through the combination of the behaviors of individual actors in the network. Usually the growth of such networks is fueled by an ideology or sociological force that is adhered to or shared by all participants in the network.[original research?][citation needed]

In economics

In economics, a market economy is sometimes said to be self-organizing. Paul Krugman has written on the role that market self-organization plays in the business cycle in his book "The Self Organizing Economy"[25]. Friedrich Hayek coined the term catallaxy to describe a "self-organizing system of voluntary co-operation," in regard to capitalism. Most modern economists hold that imposing central planning usually makes the self-organized economic system less efficient. By contrast, some socialist economists consider that market failures are so significant that self-organization produces bad results and that the state should direct production and pricing. Many economists adopt an intermediate position and recommend a mixture of market economy and command economy characteristics (sometimes called a mixed economy). When applied to economics, the concept of self-organization can quickly become ideologically-imbued (as explained in chapter 5 of A. Marshall, The Unity of Nature, Imperial College Press, 2002).

In collective intelligence

Visualization of links between pages on a wiki. This is an example of collective intelligence through collaborative editing.
Non-thermodynamic concepts of entropy and self-organization have been explored by many theorists. Cliff Joslyn and colleagues and their so-called "global brain" projects. Marvin Minsky's "Society of Mind" and the no-central editor in charge policy of the open sourced internet encyclopedia, called Wikipedia, are examples of applications of these principles - see collective intelligence.
Donella Meadows, who codified twelve leverage points that a self-organizing system could exploit to organize itself, was one of a school of theorists who saw human creativity as part of a general process of adapting human lifeways to the planet and taking humans out of conflict with natural processes. See Gaia philosophy, deep ecology, ecology movement and Green movement for similar self-organizing ideals. (The connections between self-organisation and Gaia theory and the environmental movement are explored in A. Marshall, 2002, The Unity of Nature, Imperial College Press: London).

Self-organization in linguistics

Self-organization refers to a property by which complex systems spontaneously generate organized structures"[26].[Full citation needed] It is the spontaneous formation of well organized structures, patterns, or behaviors, from random initial conditions. It is the process of macroscopic outcomes emerging from local interactions of components of the system, but the global organizational properties are not to be found at the local level. The systems used to study this phenomenon are referred to as dynamical systems: state-determined systems. They possess a large number of elements or variables, and thus very large state spaces.
Traditional framework of good science is Reductionism, in the sense that sub-parts are studied individually to understand the bigger part. However, many natural systems cannot simply be explained by a reductionist study of their parts. Self-organization is not studying the whole structure by breaking it down to smaller sub-parts which are then studied individually. The emphasis of the “self-organization” is, rather, the process of how a super macro global structure evolves from local interactions.
"The self that gets organized should not be just the language ability but the cluster of competencies through which it emerges. These probably include a variety of cognitive, social, affective, and motor skills."[27][Full citation needed] The human brains, and thus the phenomena of sensation and thought, are also under the strong influence of features of spontaneous organization in their structure. Indeed, the brain, composed of billions of neurons dynamically interacting among themselves and with the outside world, is the prototype of a complex system. A good example of self organization in linguistics is the evolution of Nicaraguan Sign Language. Examples of linguistic questions in the light of self organization are: e.g. the decentralized generation of lexical and semantic conventions in populations of agents.[28][Full citation needed][29][Full citation needed];the formation of conventionalized syntactic structures[30];[Full citation needed] the conditions under which combinatoriality, the property of systematic reuse, can be selected[31];[Full citation needed] shared inventories of vowels or syllables in groups of agents, with features of structural regularities greatly resembling those of human languages[32][Full citation needed][33][Full citation needed]


In many complex systems in nature, there are global phenomena that are the irreducible result of local interactions between components whose individual study would not allow us to see the global properties of the whole combined system. Thus, a growing number of researchers think that many properties of language are not directly encoded by any of the components involved, but are the self-organized outcomes of the interactions of the components.
Building mathematical models in the context of research into language origins and the evolution of languages is enjoying growing popularity in the scientific community, because it is a crucial tool for studying the phenomena of language in relation to the complex interactions of its components. These systems are put to two main types of use: 1) they serve to evaluate the internal coherence of verbally expressed theories already proposed by clarifying all their hypotheses and verifying that they do indeed lead to the proposed conclusions ; 2) they serve to explore and generate new theories, which themselves often appear when one simply tries to build an artificial system reproducing the verbal behavior of humans.
Therefore, constructing operational models to test hypothesis in linguistics is gaining popularity these days. An operational model is one which defines the set of its assumptions explicitly and above all shows how to calculate their consequences, that is, to prove that they lead to a certain set of conclusions.

[edit] In the emergence of language

The emergence of language in the human species has been described in a game-theoretic framework based on a model of senders and receivers of information (Clark 2009[34], following Skyrms 2004[35]).[Full citation needed] The evolution of certain properties of language such as inference follow from this sort of framework (with the parameters stating that information transmitted can be partial or redundant, and the underlying assumption that the sender and receiver each want to take the action in his/her best interest) [36].[Full citation needed] Likewise, models have shown that compositionality, a central component of human language, emerges dynamically during linguistic evolution, and need not be introduced by biological evolution (Kirby 2000)[37].[Full citation needed] Tomasello (1999)[38][Full citation needed] argues that through one evolutionary step, the ability to sustain culture, the groundwork for the evolution of human language was laid. The ability to ratchet cultural advances cumulatively allowed for the complex development of human cognition unseen in other animals.

[edit] In language acquisition

Within a species' ontogeny, the acquisition of language has also been shown to self-organize. Through the ability to see others as intentional agents (theory of mind), and actions such as 'joint attention,' human children have the scaffolding they need to learn the language of those around them (Tomasello 1999)[39].[Full citation needed]

In articulatory phonology

Articulatory phonology takes the approach that speech production consists of a coordinated series of gestures, called 'constellations,' which are themselves dynamical systems. In this theory, linguistic contrast comes from the distinction between such gestural units, which can be described on a low-dimensional level in the abstract. However, these structures are necessarily context-dependent in real-time production. Thus the context-dependence emerges naturally from the dynamical systems themselves. This statement is controversial, however, as it suggests a universal phonetics which is not evident across languages[40]. Cross-linguistic patterns show that what can be treated as the same gestural units produce different contextualised patterns in different languages[41]. Articulatory Phonology fails to attend to the acoustic output of the gestures themselves (meaning that many typological patterns remain unexplained)[42]. Freedom among listeners in the weighting of perceptual cues in the acoustic signal has a more fundamental role to play in the emergence of structure[43]. The realization of the perceptual contrasts by means of articulatory movements means that articulatory considerations do play a role[44], but these are purely secondary.

In diachrony and synchrony

Several mathematical models of language change rely on self-organizing or dynamical systems. Abrams and Strogatz (2003)[45][Full citation needed] produced a model of language change that focused on “language death” - the process by which a speech community merges into the surrounding speech communities. Nakamura et al. (2008)[46][Full citation needed] proposed a variant of this model that incorporates spatial dynamics into language contact transactions in order to describe the emergence of creoles. Both of these models proceed from the assumption that language change, like any self-organizing system, is a large-scale act or entity (in this case the creation or death of a language, or changes in its boundaries) that emerges from many actions on a micro-level. The microlevel in this example is the everyday production and comprehension of language by speakers in areas of language contact.

See also


  1. ^ Glansdorff, P., Prigogine, I. (1971). Thermodynamic Theory of Structure, Stability and Fluctuations, Wiley-Interscience, London. ISBN 0471302805
  2. ^ Eric. Bonabeau, Marco Dorigo, and Guy Theraulaz (1999). Swarm intelligence: from natural to artificial systems. pp.9-11.
  3. ^ Ashby, W.R., (1947): Principles of the Self-Organizing Dynamic System, In: Journal of General Psychology 1947. volume 37, pages 125--128
  4. ^ As an indication of the increasing importance of this concept, when queried with the keyword self-organ*, Dissertation Abstracts finds nothing before 1954, and only four entries before 1970. There were 17 in the years 1971--1980; 126 in 1981--1990; and 593 in 1991--2000.
  5. ^ Self-organized theory in quantum gravity
  6. ^ “Thermodynamics, Nonequilibrium,” Glansdorff, P. & Prigogine, I. The Encyclopedia of Physics, Second Edition, edited by Lerner, R. and Trigg, G., VCH Publishers, 1991. Pp. 1256-1262.
  7. ^ “Lasers,” Zeiger, H.J. and Kelley, P.L. The Encyclopedia of Physics, Second Edition, edited by Lerner, R. and Trigg, G., VCH Publishers, 1991. Pp. 614-619.
  8. ^ M. Strong (2004). "Protein Nanomachines". PLoS Biol. 2 (3): e73-e74. doi:10.1371/journal.pbio.0020073. 
  9. ^ Camazine, Deneubourg, Franks, Sneyd, Theraulaz, Bonabeau, Self-Organization in Biological Systems, Princeton University Press, 2003. ISBN 0-691-11624-5 --ISBN 0-691-01211-3 (pbk.) p. 8
  10. ^ Daniel Dennett (1995), Darwin's Dangerous Idea, Penguin Books, London, ISBN 978-0-14-016734-4, ISBN 0-14-016734-X
  11. ^ The mathematics of self-organising systems. Recent developments in information and decision processes, Macmillan, N. Y., 1962.
  12. ^ Cybernetics, or control and communication in the animal and the machine, The MIT Press, Cambridge, Mass. and Wiley, N.Y., 1948. 2nd Edition 1962 "Chapter X "Brain Waves and Self-Organizing Systems"pp 201-202.
  13. ^ "Design for a Brain" Chapter 5 Chapman & Hall (1952) and "An Introduction to Cybernetics" Chapman & Hall (1956)
  14. ^ "An Introduction to Cybernetics" Part Two Chapman & Hall (1956)
  15. ^ Conant and Ashby Int. J. Systems Sci., 1970, vol 1, No 2, pp89-97 and in "Mechanisms of Intelligence" ed Roger Conant Intersystems Publications (1981)
  16. ^ "Embodiments of Mind MIT Press (1965)"
  17. ^ "A Predictive Model for Self-Organizing Systems", Part I: Cybernetica 3, pp. 258–300; Part II: Cybernetica 4, pp. 20–55, 1961 with Gordon Pask.
  18. ^ "Brain of the Firm" Alan Lane (1972) see also Viable System Model also in "Beyond Dispute " Wiley Stafford Beer 1994 "Redundancy of Potential Command" pp157-158.
  19. ^ see "Brain.." and "Beyond Dispute"
  20. ^ * 1996, Heinz von Foerster's Self-Organisation, the Progenitor of Conversation and Interaction Theories, Systems Research (1996) 13, 3, pp. 349-362
  21. ^ "Conversation, Cognition and Learning" Elesevier (1976) see Glossary.
  22. ^ "On Gordon Pask" Nick Green in "Gordon Pask remembered and celebrated: Part I" Kybernetes 30, 5/6, 2001 p 676 (a.k.a. Pask's self-described "Last Theorem")
  23. ^ proof para. 188 Pask (1992) and postulates 15-18 in Pask (1996)
  24. ^ Interactive models
  25. ^ "The Self Organizing Economy". 1996.
  26. ^ de Boer, B, 1998
  27. ^ Wimsatt, p. 232, Cycles of Contingency
  28. ^ Steels, 1997
  29. ^ Kaplan, 2001
  30. ^ Batali, 1998
  31. ^ Kirby, 1998
  32. ^ de Boer, 2001
  33. ^ Oudeyer, 2001
  34. ^ Clark 2009
  35. ^ Skyrms 2004
  36. ^ (Skyrms 2004)
  37. ^ Kirby 2000
  38. ^ Tomasello (1999)
  39. ^ Tomasello 1999
  40. ^ Sole, M-J. (1992). "Phonetic and phonological processes: nasalization." Language & Speech 35: 29-43
  41. ^ Ladefoged, Peter (2003). "Commentary: some thoughts on syllables - an old-fashioned interlude." In Local, John, Richard Ogden & Ros Temple (eds.). Papers in laboratory Phonology VICambridge University Press: 269-276.
  42. ^ see papers in Phonetica 49, 1992, special issue on Articulatory Phonology
  43. ^ Ohala, John J. (1996) "Speech perception is hearing sounds, not tongues." Journal of the Acoustical Society of America 99: 1718-1725.
  44. ^ Lindblom, B. (1999). "Emergent phonology.", doi=
  45. ^ Abrams and Strogatz (2003)
  46. ^ Nakamura et al. (2008)

Further reading

  • W. Ross Ashby (1947), "Principles of the Self-Organizing Dynamic System", Journal of General Psychology Vol 37, pp. 125–128.
  • W. Ross Ashby (1966), Design for a Brain, Chapman & Hall, 2nd edition.
  • Per Bak (1996), How Nature Works: The Science of Self-Organized Criticality, Copernicus Books.
  • Philip Ball (1999), The Self-Made Tapestry: Pattern Formation in Nature, Oxford University Press.
  • Stafford Beer, Self-organization as autonomy: Brain of the Firm 2nd edition Wiley 1981 and Beyond Dispute Wiley 1994.
  • A. Bejan (2000), Shape and Structure, from Engineering to Nature , Cambridge University Press, Cambridge, UK, 324 pp.
  • Mark Buchanan (2002), Nexus: Small Worlds and the Groundbreaking Theory of Networks W. W. Norton & Company.
  • Scott Camazine, Jean-Louis Deneubourg, Nigel R. Franks, James Sneyd, Guy Theraulaz, & Eric Bonabeau (2001) Self-Organization in Biological Systems, Princeton Univ Press.
  • Falko Dressler (2007), Self-Organization in Sensor and Actor Networks, Wiley & Sons.
  • Manfred Eigen and Peter Schuster (1979), The Hypercycle: A principle of natural self-organization, Springer.
  • Myrna Estep (2003), A Theory of Immediate Awareness: Self-Organization and Adaptation in Natural Intelligence, Kluwer Academic Publishers.
  • Myrna L. Estep (2006), Self-Organizing Natural Intelligence: Issues of Knowing, Meaning, and Complexity, Springer-Verlag.
  • J. Doyne Farmer et al. (editors) (1986), "Evolution, Games, and Learning: Models for Adaptation in Machines and Nature", in: Physica D, Vol 22.
  • Heinz von Foerster and George W. Zopf, Jr. (eds.) (1962), Principles of Self-Organization (Sponsored by Information Systems Branch, U.S. Office of Naval Research.
  • "Aeshchines" (false identity made in reference to the classical Greek orator Aeschines) (2007). "The Open Source Manifesto" the self organization of economic and geopolitical structure through the Open Source movement permanent link at
  • Carlos Gershenson and Francis Heylighen (2003). "When Can we Call a System Self-organizing?" In Banzhaf, W, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler, Advances in Artificial Life, 7th European Conference, ECAL 2003, Dortmund, Germany, pp. 606–614. LNAI 2801. Springer.
  • Hermann Haken (1983) Synergetics: An Introduction. Nonequilibrium Phase Transition and Self-Organization in Physics, Chemistry, and Biology, Third Revised and Enlarged Edition, Springer-Verlag.
  • F.A. Hayek Law, Legislation and Liberty, RKP, UK.
  • Francis Heylighen (2001): "The Science of Self-organization and Adaptivity".
  • Henrik Jeldtoft Jensen (1998), Self-Organized Criticality: Emergent Complex Behaviour in Physical and Biological Systems, Cambridge Lecture Notes in Physics 10, Cambridge University Press.
  • Steven Berlin Johnson (2001), Emergence: The Connected Lives of Ants, Brains, Cities and Software.
  • Stuart Kauffman (1995), At Home in the Universe, Oxford University Press.
  • Stuart Kauffman (1993), Origins of Order: Self-Organization and Selection in Evolution Oxford University Press.
  • J. A. Scott Kelso (1995), Dynamic Patterns: The self-organization of brain and behavior, The MIT Press, Cambridge, MA.
  • J. A. Scott Kelso & David A Engstrom (2006), "The Complementary Nature", The MIT Press, Cambridge, MA.
  • Alex Kentsis (2004), Self-organization of biological systems: Protein folding and supramolecular assembly, Ph.D. Thesis, New York University.
  • E.V.Krishnamurthy(2009)," Multiset of Agents in a Network for Simulation of Complex Systems", in "Recent advances in Nonlinear Dynamics and synchronization, ,(NDS-1) -Theory and applications, Springer Verlag, New York,2009. Eds. K.Kyamakya et al.
  • Paul Krugman (1996), The Self-Organizing Economy, Cambridge, Mass., and Oxford: Blackwell Publishers.
  • Niklas Luhmann (1995) Social Systems. Stanford, CA: Stanford University Press.
  • Elizabeth McMillan (2004) "Complexity, Organizations and Change".
  • Marshall, A (2002) The Unity of Nature, Imperial College Press: London (esp. chapter 5)
  • Müller, J.-A., Lemke, F. (2000), Self-Organizing Data Mining.
  • Gregoire Nicolis and Ilya Prigogine (1977) Self-Organization in Non-Equilibrium Systems, Wiley.
  • Heinz Pagels (1988), The Dreams of Reason: The Computer and the Rise of the Sciences of Complexity, Simon & Schuster.
  • Gordon Pask (1961), The cybernetics of evolutionary processes and of self organizing systems, 3rd. International Congress on Cybernetics, Namur, Association Internationale de Cybernetique.
  • Gordon Pask (1993) Interactions of Actors (IA), Theory and Some Applications, Download incomplete 90 page manuscript.
  • Gordon Pask (1996) Heinz von Foerster's Self-Organisation, the Progenitor of Conversation and Interaction Theories, Systems Research (1996) 13, 3, pp. 349–362
  • Christian Prehofer ea. (2005), "Self-Organization in Communication Networks: Principles and Design Paradigms", in: IEEE Communications Magazine, July 2005.
  • Mitchell Resnick (1994), Turtles, Termites and Traffic Jams: Explorations in Massively Parallel Microworlds, Complex Adaptive Systems series, MIT Press.
  • Lee Smolin (1997), The Life of the Cosmos Oxford University Press.
  • Ricard V. Solé and Brian C. Goodwin (2001), Signs of Life: How Complexity Pervades Biology, Basic Books.
  • Ricard V. Solé and Jordi Bascompte (2006), Selforganization in Complex Ecosystems, Princeton U. Press
  • Steven Strogatz (2004), Sync: The Emerging Science of Spontaneous Order, Theia.
  • D'Arcy Thompson (1917), On Growth and Form, Cambridge University Press, 1992 Dover Publications edition.
  • Norbert Wiener (1962), The mathematics of self-organising systems. Recent developments in information and decision processes, Macmillan, N. Y. and Chapter X in Cybernetics, or control and communication in the animal and the machine, The MIT Press, 2nd Edition 1962
  • Tom De Wolf, Tom Holvoet (2005), Emergence Versus Self-Organisation: Different Concepts but Promising When Combined, In Engineering Self Organising Systems: Methodologies and Applications, Lecture Notes in Computer Science, volume 3464, pp 1–15.
  • Tsekeris, Charalambos and Konstantinos Koskinas (2010) "A Weak Reflection on Unpredictability and Social Theory", tripleC – Cognition, Communication, Co-operation: Open Access Journal for a Global Sustainable Information Society, 8, 1, pp. 36-42.
  • K. Yee (2003), "Ownership and Trade from Evolutionary Games," International Review of Law and Economics, 23.2, 183-197.
  • Louise B. Young (2002), The Unfinished Universe
  • Mikhail Prokopenko (ed.) (2008), Advances in Applied Self-organizing Systems, Springer.

[edit] External links

Dissertations and Theses on Self-organization