Negative feedback (updated)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Negative_feedback

 

A simple negative feedback system descriptive, for example, of some electronic amplifiers. The feedback is negative if the loop gain AB is negative.

Negative feedback (or balancing feedback) occurs when some function of the output of a system, process, or mechanism is fed back in a manner that tends to reduce the fluctuations in the output, whether caused by changes in the input or by other disturbances. 

Whereas positive feedback tends to lead to instability via exponential growth, oscillation or chaotic behavior, negative feedback generally promotes stability. Negative feedback tends to promote a settling to equilibrium, and reduces the effects of perturbations. Negative feedback loops in which just the right amount of correction is applied with optimum timing can be very stable, accurate, and responsive.

Negative feedback is widely used in mechanical and electronic engineering, and also within living organisms, and can be seen in many other fields from chemistry and economics to physical systems such as the climate. General negative feedback systems are studied in control systems engineering.

Negative feedback loops also play an integral role in maintaining the atmospheric balance in various systems on Earth. One such feedback system is the interaction between solar radiation, cloud cover, and planet temperature. 

Blood glucose levels are maintained at a constant level in the body by a negative feedback mechanism. When the blood glucose level is too high, the pancreas secretes insulin and when the level is too low, the pancreas then secretes glucagon. The flat line shown represents the homeostatic set point. The sinusoidal line represents the blood glucose level.

 

Examples

• Mercury thermostats (circa 1600) using expansion and contraction of columns of mercury in response to temperature changes were used in negative feedback systems to control vents in furnaces, maintaining a steady internal temperature.
• In the invisible hand of the market metaphor of economic theory (1776), reactions to price movements provide a feedback mechanism to match supply and demand.
• In centrifugal governors (1788), negative feedback is used to maintain a near-constant speed of an engine, irrespective of the load or fuel-supply conditions.
• In a steering engine (1866), power assistance is applied to the rudder with a feedback loop, to maintain the direction set by the steersman.
• In servomechanisms, the speed or position of an output, as determined by a sensor, is compared to a set value, and any error is reduced by negative feedback to the input.
• In audio amplifiers, negative feedback reduces distortion, minimises the effect of manufacturing variations in component parameters, and compensates for changes in characteristics due to temperature change.
• In analog computing feedback around operational amplifiers is used to generate mathematical functions such as addition, subtraction, integration, differentiation, logarithm, and antilog functions.
• In a phase locked loop (1932) feedback is used to maintain a generated alternating waveform in a constant phase to a reference signal. In many implementations the generated waveform is the output, but when used as a demodulator in an FM radio receiver, the error feedback voltage serves as the demodulated output signal. If there is a frequency divider between the generated waveform and the phase comparator, the device acts as a frequency multiplier.
• In organisms, feedback enables various measures (e.g. body temperature, or blood sugar level) to be maintained within a desired range by homeostatic processes.

History

Negative feedback as a control technique may be seen in the refinements of the water clock introduced by Ktesibios of Alexandria in the 3rd century BCE. Self-regulating mechanisms have existed since antiquity, and were used to maintain a constant level in the reservoirs of water clocks as early as 200 BCE.

The fly-ball governor is an early example of negative feedback.

 

Negative feedback was implemented in the 17th Century. Cornelius Drebbel had built thermostatically-controlled incubators and ovens in the early 1600s, and centrifugal governors were used to regulate the distance and pressure between millstones in windmills. James Watt patented a form of governor in 1788 to control the speed of his steam engine, and James Clerk Maxwell in 1868 described "component motions" associated with these governors that lead to a decrease in a disturbance or the amplitude of an oscillation.

The term "feedback" was well established by the 1920s, in reference to a means of boosting the gain of an electronic amplifier. Friis and Jensen described this action as "positive feedback" and made passing mention of a contrasting "negative feed-back action" in 1924. Harold Stephen Black came up with the idea of using negative feedback in electronic amplifiers in 1927, submitted a patent application in 1928, and detailed its use in his paper of 1934, where he defined negative feedback as a type of coupling that reduced the gain of the amplifier, in the process greatly increasing its stability and bandwidth.

Karl Küpfmüller published papers on a negative-feedback-based automatic gain control system and a feedback system stability criterion in 1928.

Nyquist and Bode built on Black’s work to develop a theory of amplifier stability.

Early researchers in the area of cybernetics subsequently generalized the idea of negative feedback to cover any goal-seeking or purposeful behavior.

All purposeful behavior may be considered to require negative feed-back. If a goal is to be attained, some signals from the goal are necessary at some time to direct the behavior.

Cybernetics pioneer Norbert Wiener helped to formalize the concepts of feedback control, defining feedback in general as "the chain of the transmission and return of information", and negative feedback as the case when:

The information fed back to the control center tends to oppose the departure of the controlled from the controlling quantity...

While the view of feedback as any "circularity of action" helped to keep the theory simple and consistent, Ashby pointed out that, while it may clash with definitions that require a "materially evident" connection, "the exact definition of feedback is nowhere important". Ashby pointed out the limitations of the concept of "feedback":

The concept of 'feedback', so simple and natural in certain elementary cases, becomes artificial and of little use when the interconnections between the parts become more complex...Such complex systems cannot be treated as an interlaced set of more or less independent feedback circuits, but only as a whole. For understanding the general principles of dynamic systems, therefore, the concept of feedback is inadequate in itself. What is important is that complex systems, richly cross-connected internally, have complex behaviors, and that these behaviors can be goal-seeking in complex patterns.

To reduce confusion, later authors have suggested alternative terms such as degenerative, self-correcting, balancing, or discrepancy-reducing in place of "negative". 

Overview

Feedback loops in the human body

 

In many physical and biological systems, qualitatively different influences can oppose each other. For example, in biochemistry, one set of chemicals drives the system in a given direction, whereas another set of chemicals drives it in an opposing direction. If one or both of these opposing influences are non-linear, equilibrium point(s) result.

In biology, this process (in general, biochemical) is often referred to as homeostasis; whereas in mechanics, the more common term is equilibrium.

In engineering, mathematics and the physical, and biological sciences, common terms for the points around which the system gravitates include: attractors, stable states, eigenstates/eigenfunctions, equilibrium points, and setpoints.

In control theory, negative refers to the sign of the multiplier in mathematical models for feedback. In delta notation, −Δoutput is added to or mixed into the input. In multivariate systems, vectors help to illustrate how several influences can both partially complement and partially oppose each other.

Some authors, in particular with respect to modelling business systems, use negative to refer to the reduction in difference between the desired and actual behavior of a system. In a psychology context, on the other hand, negative refers to the valence of the feedback – attractive versus aversive, or praise versus criticism.

In contrast, positive feedback is feedback in which the system responds so as to increase the magnitude of any particular perturbation, resulting in amplification of the original signal instead of stabilization. Any system in which there is positive feedback together with a gain greater than one will result in a runaway situation. Both positive and negative feedback require a feedback loop to operate.

However, negative feedback systems can still be subject to oscillations. This is caused by the slight delays around any loop. Due to these delays the feedback signal of some frequencies can arrive one half cycle later which will have a similar effect to positive feedback and these frequencies can reinforce themselves and grow over time. This problem is often dealt with by attenuating or changing the phase of the problematic frequencies. Unless the system naturally has sufficient damping, many negative feedback systems have low pass filters or dampers fitted. 

Some specific implementations

There are a large number of different examples of negative feedback and some are discussed below. 

Error-controlled regulation

Basic error-controlled regulator loop

A regulator R adjusts the input to a system T so the monitored essential variables E are held to set-point values S that result in the desired system output despite disturbances D.

 

One use of feedback is to make a system (say T) self-regulating to minimize the effect of a disturbance (say D). Using a negative feedback loop, a measurement of some variable (for example, a process variable, say E) is subtracted from a required value (the 'set point') to estimate an operational error in system status, which is then used by a regulator (say R) to reduce the gap between the measurement and the required value. The regulator modifies the input to the system T according to its interpretation of the error in the status of the system. This error may be introduced by a variety of possible disturbances or 'upsets', some slow and some rapid. The regulation in such systems can range from a simple 'on-off' control to a more complex processing of the error signal.

It may be noted that the physical form of the signals in the system may change from point to point. So, for example, a change in weather may cause a disturbance to the heat input to a house (as an example of the system T) that is monitored by a thermometer as a change in temperature (as an example of an 'essential variable' E), converted by the thermostat (a 'comparator') into an electrical error in status compared to the 'set point' S, and subsequently used by the regulator (containing a 'controller' that commands gas control valves and an ignitor) ultimately to change the heat provided by a furnace (an 'effector') to counter the initial weather-related disturbance in heat input to the house.

Error controlled regulation is typically carried out using a Proportional-Integral-Derivative Controller (PID controller). The regulator signal is derived from a weighted sum of the error signal, integral of the error signal, and derivative of the error signal. The weights of the respective components depend on the application.

Mathematically, the regulator signal is given by:

${\displaystyle \mathrm {MV(t)} =K_{p}\left(\,{e(t)}+{\frac {1}{T_{i}}}\int _{0}^{t}{e(\tau )}\,{d\tau }+T_{d}{\frac {d}{dt}}e(t)\right)}$

where

${\displaystyle T_{i}}$ is the integral time

${\displaystyle T_{d}}$ is the derivative time

Negative feedback amplifier

The negative feedback amplifier was invented by Harold Stephen Black at Bell Laboratories in 1927, and granted a patent in 1937 (US Patent 2,102,671 "a continuation of application Serial No. 298,155, filed August 8, 1928 ...").

"The patent is 52 pages long plus 35 pages of figures. The first 43 pages amount to a small treatise on feedback amplifiers!"

There are many advantages to feedback in amplifiers. In design, the type of feedback and amount of feedback are carefully selected to weigh and optimize these various benefits. 

Advantages of negative voltage feedback in amplifiers are-

 

 1) It reduces non linear distortion that is it has higher fidelity.
 2) It increases circuit stability that is the gain remains stable though

    there are variations in ambient temperature, frequency and signal amplitude.
 3) It increases bandwidth that is the frequency response is improved.
 4) It is possible to modify the input and output impedances.
 5) The harmonic distortion, phase distortion are less.
 6) The amplitude and frequency distortion are less.
 7) Noise is reduced considerably.
 8) An important advantage of negative feedback is that it stabilizes the gain.

Though negative feedback has many advantages, amplifiers with feedback can oscillate. See the article on step response. They may even exhibit instability. Harry Nyquist of Bell Laboratories proposed the Nyquist stability criterion and the Nyquist plot that identify stable feedback systems, including amplifiers and control systems. 

Negative feedback amplifier with external disturbance. The feedback is negative if βA >0.

 

The figure shows a simplified block diagram of a negative feedback amplifier. 

The feedback sets the overall (closed-loop) amplifier gain at a value:

${\displaystyle {\frac {O}{I}}={\frac {A}{1+\beta A}}\approx {\frac {1}{\beta }}\ ,}$

where the approximate value assumes βA >> 1. This expression shows that a gain greater than one requires β < 1. Because the approximate gain 1/β is independent of the open-loop gain A, the feedback is said to 'desensitize' the closed-loop gain to variations in A (for example, due to manufacturing variations between units, or temperature effects upon components), provided only that the gain A is sufficiently large. In this context, the factor (1+βA) is often called the 'desensitivity factor', and in the broader context of feedback effects that include other matters like electrical impedance and bandwidth, the 'improvement factor'.

If the disturbance D is included, the amplifier output becomes:

${\displaystyle O={\frac {AI}{1+\beta A}}+{\frac {D}{1+\beta A}}\ ,}$

which shows that the feedback reduces the effect of the disturbance by the 'improvement factor' (1+β A). The disturbance D might arise from fluctuations in the amplifier output due to noise and nonlinearity (distortion) within this amplifier, or from other noise sources such as power supplies.

The difference signal I–βO at the amplifier input is sometimes called the "error signal". According to the diagram, the error signal is:

${\displaystyle {\text{Error signal}}=I-\beta O=I\left(1-\beta {\frac {O}{I}}\right)={\frac {I}{1+\beta A}}-{\frac {\beta D}{1+\beta A}}\ .}$

From this expression, it can be seen that a large 'improvement factor' (or a large loop gain βA) tends to keep this error signal small. 

Although the diagram illustrates the principles of the negative feedback amplifier, modeling a real amplifier as a unilateral forward amplification block and a unilateral feedback block has significant limitations. For methods of analysis that do not make these idealizations, see the article Negative feedback amplifier. 

Operational amplifier circuits

A feedback voltage amplifier using an op amp with finite gain but infinite input impedances and zero output impedance.

 

The operational amplifier was originally developed as a building block for the construction of analog computers, but is now used almost universally in all kinds of applications including audio equipment and control systems.

Operational amplifier circuits typically employ negative feedback to get a predictable transfer function. Since the open-loop gain of an op-amp is extremely large, a small differential input signal would drive the output of the amplifier to one rail or the other in the absence of negative feedback. A simple example of the use of feedback is the op-amp voltage amplifier shown in the figure.

The idealized model of an operational amplifier assumes that the gain is infinite, the input impedance is infinite, output resistance is zero, and input offset currents and voltages are zero. Such an ideal amplifier draws no current from the resistor divider. Ignoring dynamics (transient effects and propagation delay), the infinite gain of the ideal op-amp means this feedback circuit drives the voltage difference between the two op-amp inputs to zero. Consequently, the voltage gain of the circuit in the diagram, assuming an ideal op amp, is the reciprocal of feedback voltage division ratio β:

${\displaystyle V_{\text{out}}={\frac {R_{\text{1}}+R_{\text{2}}}{R_{\text{1}}}}V_{\text{in}}\!={\frac {1}{\beta }}V_{\text{in}}\,}$.

A real op-amp has a high but finite gain A at low frequencies, decreasing gradually at higher frequencies. In addition, it exhibits a finite input impedance and a non-zero output impedance. Although practical op-amps are not ideal, the model of an ideal op-amp often suffices to understand circuit operation at low enough frequencies. As discussed in the previous section, the feedback circuit stabilizes the closed-loop gain and desensitizes the output to fluctuations generated inside the amplifier itself.

Mechanical engineering

The ballcock or float valve uses negative feedback to control the water level in a cistern.

 

An example of the use of negative feedback control is the ballcock control of water level (see diagram). In modern engineering, negative feedback loops are found in fuel injection systems and carburettors. Similar control mechanisms are used in heating and cooling systems, such as those involving air conditioners, refrigerators, or freezers. 

Biology

Control of endocrine hormones by negative feedback.

 

Some biological systems exhibit negative feedback such as the baroreflex in blood pressure regulation and erythropoiesis. Many biological process (e.g., in the human anatomy) use negative feedback. Examples of this are numerous, from the regulating of body temperature, to the regulating of blood glucose levels. The disruption of feedback loops can lead to undesirable results: in the case of blood glucose levels, if negative feedback fails, the glucose levels in the blood may begin to rise dramatically, thus resulting in diabetes.

For hormone secretion regulated by the negative feedback loop: when gland X releases hormone X, this stimulates target cells to release hormone Y. When there is an excess of hormone Y, gland X "senses" this and inhibits its release of hormone X. As shown in the figure, most endocrine hormones are controlled by a physiologic negative feedback inhibition loop, such as the glucocorticoids secreted by the adrenal cortex. The hypothalamus secretes corticotropin-releasing hormone (CRH), which directs the anterior pituitary gland to secrete adrenocorticotropic hormone (ACTH). In turn, ACTH directs the adrenal cortex to secrete glucocorticoids, such as cortisol. Glucocorticoids not only perform their respective functions throughout the body but also negatively affect the release of further stimulating secretions of both the hypothalamus and the pituitary gland, effectively reducing the output of glucocorticoids once a sufficient amount has been released.

Chemistry

Closed systems containing substances undergoing a reversible chemical reaction can also exhibit negative feedback in accordance with Le Chatelier's principle which shift the chemical equilibrium to the opposite side of the reaction in order to reduce a stress. For example in the reaction

N₂ + 3 H₂ ⇌ 2 NH₃ + 92 kJ/mol

If a mixture of the reactants and products exists at equilibrium in a sealed container and nitrogen gas is added to this system, then the equilibrium will shift toward the product side in response. If the temperature is raised, then the equilibrium will shift toward the reactant side which, because the reverse reaction is endothermic, will partially reduces the temperature.

Self-organization

Self-organization is the capability of certain systems "of organizing their own behavior or structure". There are many possible factors contributing to this capacity, and most often positive feedback is identified as a possible contributor. However, negative feedback also can play a role.

Economics

In economics, automatic stabilisers are government programs that are intended to work as negative feedback to dampen fluctuations in real GDP. 

Mainstream economics asserts that the market pricing mechanism operates to match supply and demand, because mismatches between them feed back into the decision-making of suppliers and demanders of goods, altering prices and thereby reducing any discrepancy. However Norbert Wiener wrote in 1948:

"There is a belief current in many countries and elevated to the rank of an official article of faith in the United States that free competition is itself a homeostatic process... Unfortunately the evidence, such as it is, is against this simple-minded theory."

The notion of economic equilibrium being maintained in this fashion by market forces has also been questioned by numerous heterodox economists such as financier George Soros and leading ecological economist and steady-state theorist Herman Daly, who was with the World Bank in 1988-1994.

Environmental Applications

A basic and common example of a negative feedback system in the environment is the interaction among cloud cover, plant growth, solar radiation, and planet temperature. As incoming solar radiation increases, planet temperature increases. As the temperature increases, the amount of plant life that can grow increases. This plant life can then make products such as sulfur which produce more cloud cover. An increase in cloud cover leads to higher albedo, or surface reflectivity, of the Earth. As albedo increases, however, the amount of solar radiation decreases. This, in turn, affects the rest of the cycle. 

Cloud cover, and in turn planet albedo and temperature, is also influenced by the hydrological cycle. As planet temperature increases, more water vapor is produced, creating more clouds. The clouds then block incoming solar radiation, lowering the temperature of the planet. This interaction produces less water vapor and therefore less cloud cover. The cycle then repeats in a negative feedback loop. In this way, negative feedback loops in the environment are stabilizing.

Cybernetics (updated)

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cybernetics

 

Principle diagram of a cybernetic system with a feedback loop

Cybernetics is a transdisciplinary approach for exploring regulatory systems—their structures, constraints, and possibilities. Norbert Wiener defined cybernetics in 1948 as "the scientific study of control and communication in the animal and the machine." In other words, it is the scientific study of how humans, animals and machines control and communicate with each other. 

Cybernetics is applicable when a system being analyzed incorporates a closed signaling loop—originally referred to as a "circular causal" relationship—that is, where action by the system generates some change in its environment and that change is reflected in the system in some manner (feedback) that triggers a system change. Cybernetics is relevant to, for example, mechanical, physical, biological, cognitive, and social systems. The essential goal of the broad field of cybernetics is to understand and define the functions and processes of systems that have goals and that participate in circular, causal chains that move from action to sensing to comparison with desired goal, and again to action. Its focus is how anything (digital, mechanical or biological) processes information, reacts to information, and changes or can be changed to better accomplish the first two tasks. Cybernetics includes the study of feedback, black boxes and derived concepts such as communication and control in living organisms, machines and organizations including self-organization. 

Concepts studied by cyberneticists include, but are not limited to: learning, cognition, adaptation, social control, emergence, convergence, communication, efficiency, efficacy, and connectivity. In cybernetics these concepts (otherwise already objects of study in other disciplines such as biology and engineering) are abstracted from the context of the specific organism or device. 

The word cybernetics comes from Greek κυβερνητική (kybernētikḗ), meaning "governance", i.e., all that are pertinent to κυβερνάω (kybernáō), the latter meaning "to steer, navigate or govern", hence κυβέρνησις (kybérnēsis), meaning "government", is the government while κυβερνήτης (kybernḗtēs) is the governor or "helmperson" of the "ship". Contemporary cybernetics began as an interdisciplinary study connecting the fields of control systems, electrical network theory, mechanical engineering, logic modeling, evolutionary biology, neuroscience, anthropology, and psychology in the 1940s, often attributed to the Macy Conferences. During the second half of the 20th century cybernetics evolved in ways that distinguish first-order cybernetics (about observed systems) from second-order cybernetics (about observing systems). More recently there is talk about a third-order cybernetics (doing in ways that embraces first and second-order).

Studies in cybernetics provide a means for examining the design and function of any system, including social systems such as business management and organizational learning, including for the purpose of making them more efficient and effective. Fields of study which have influenced or been influenced by cybernetics include game theory, system theory (a mathematical counterpart to cybernetics), perceptual control theory, sociology, psychology (especially neuropsychology, behavioral psychology, cognitive psychology), philosophy, architecture, and organizational theory. System dynamics, originated with applications of electrical engineering control theory to other kinds of simulation models (especially business systems) by Jay Forrester at MIT in the 1950s, is a related field. 

Definitions

Cybernetics has been defined in a variety of ways, by a variety of people, from a variety of disciplines. Cybernetician Stuart Umpleby reports some notable definitions:

• "Science concerned with the study of systems of any nature which are capable of receiving, storing and processing information so as to use it for control."—A. N. Kolmogorov
• "'The art of steersmanship': deals with all forms of behavior in so far as they are regular, or determinate, or reproducible: stands to the real machine -- electronic, mechanical, neural, or economic -- much as geometry stands to real object in our terrestrial space; offers a method for the scientific treatment of the system in which complexity is outstanding and too important to be ignored."—W. Ross Ashby
• "A branch of mathematics dealing with problems of control, recursiveness, and information, focuses on forms and the patterns that connect."—Gregory Bateson
• "The art of securing efficient operation [lit.: the art of effective action]."—Louis Couffignal
• "The art of effective organization."—Stafford Beer
• "The art and science of manipulating defensible metaphors" (with relevance to constructivist epistemology. The author later extended the definition to include information flows "in all media", from stars to brains.)—Gordon Pask
• "The art of creating equilibrium in a world of constraints and possibilities."—Ernst von Glasersfeld
• "The science and art of understanding." – Humberto Maturana
• "The ability to cure all temporary truth of eternal triteness."—Herbert Brun

Other notable definitions include:

• "The science and art of the understanding of understanding."—Rodney E. Donaldson, the first president of the American Society for Cybernetics
• "A way of thinking about ways of thinking of which it is one."—Larry Richards
• "The art of interaction in dynamic networks."—Roy Ascott
• "The study of systems and processes that interact with themselves and produce themselves from themselves."—Louis Kauffman, President of the American Society for Cybernetics

Etymology

Simple feedback model. AB < 0 for negative feedback.

 

The term cybernetics stems from κυβερνήτης (cybernḗtēs) "steersman, governor, pilot, or rudder". As with the ancient Greek pilot, independence of thought is important in cybernetics. French physicist and mathematician André-Marie Ampère first coined the word "cybernetique" in his 1834 essay Essai sur la philosophie des sciences to describe the science of civil government. The term was borrowed by Norbert Wiener, in his book Cybernetics, to define the study of control and communication in the animal and the machine.

History

Roots of cybernetic theory

The word cybernetics was first used in the context of "the study of self-governance" by Plato in Alcibiades to signify the governance of people. The word 'cybernétique' was also used in 1834 by the physicist André-Marie Ampère (1775–1836) to denote the sciences of government in his classification system of human knowledge.

James Watt

The first artificial automatic regulatory system was a water clock, invented by the mechanician Ktesibios; based on a tank which poured water into a reservoir before using it to run the mechanism, it used a cone-shaped float to monitor the level of the water in its reservoir and adjust the rate of flow of the water accordingly to maintain a constant level of water in the reservoir. This was the first artificial truly automatic self-regulatory device that required no outside intervention between the feedback and the controls of the mechanism. Although they considered this part of engineering (the use of the term cybernetics is much posterior), Ktesibios and others such as Heron and Su Song are considered to be some of the first to study cybernetic principles. 

The study of teleological mechanisms (from the Greek τέλος or télos for end, goal, or purpose) in machines with corrective feedback dates from as far back as the late 18th century when James Watt's steam engine was equipped with a governor (1775–1800), a centrifugal feedback valve for controlling the speed of the engine. Alfred Russel Wallace identified this as the principle of evolution in his famous 1858 paper. In 1868 James Clerk Maxwell published a theoretical article on governors, one of the first to discuss and refine the principles of self-regulating devices. Jakob von Uexküll applied the feedback mechanism via his model of functional cycle (Funktionskreis) in order to explain animal behaviour and the origins of meaning in general. 

Early 20th century

Contemporary cybernetics began as an interdisciplinary study connecting the fields of control systems, electrical network theory, mechanical engineering, logic modeling, evolutionary biology and neuroscience in the 1940s; the ideas are also related to the biological work of Ludwig von Bertalanffy in General Systems Theory. Electronic control systems originated with the 1927 work of Bell Telephone Laboratories engineer Harold S. Black on using negative feedback to control amplifiers.

Early applications of negative feedback in electronic circuits included the control of gun mounts and radar antenna during World War II. The founder of System Dynamics, Jay Forrester, worked with Gordon S. Brown during WWII as a graduate student at the Servomechanisms Laboratory at MIT to develop electronic control systems for the U.S. Navy. Forrester later applied these ideas to social organizations, such as corporations and cities and became an original organizer of the MIT School of Industrial Management at the MIT Sloan School of Management.

W. Edwards Deming, the Total Quality Management guru for whom Japan named its top post-WWII industrial prize, was an intern at Bell Telephone Labs in 1927 and may have been influenced by network theory; Deming made "Understanding Systems" one of the four pillars of what he described as "Profound Knowledge" in his book The New Economics.

Numerous papers spearheaded the coalescing of the field. In 1935 Russian physiologist P. K. Anokhin published a book in which the concept of feedback ("back afferentation") was studied. The study and mathematical modelling of regulatory processes became a continuing research effort and two key articles were published in 1943: "Behavior, Purpose and Teleology" by Arturo Rosenblueth, Norbert Wiener, and Julian Bigelow; and the paper "A Logical Calculus of the Ideas Immanent in Nervous Activity" by Warren McCulloch and Walter Pitts.

In 1936, Ștefan Odobleja published "Phonoscopy and the clinical semiotics". In 1937, he participated in the IX International Congress of Military Medicine with "Demonstration de phonoscopie"; in the paper he disseminated a prospectus announcing his future work, "Psychologie consonantiste", the most important of his writings, where he lays the theoretical foundations of generalized cybernetics. The book, published in Paris by Librairie Maloine (vol. I in 1938 and vol. II in 1939), contains almost 900 pages and includes 300 figures in the text. The author wrote at the time that "this book is ... a table of contents, an index or a dictionary of psychology, [for] a ... great Treatise of Psychology that should contain 20–30 volumes". Due to the beginning of World War II, the publication went unnoticed (the first Romanian edition of this work did not appear until 1982).

Norbert Wiener

Cybernetics as a discipline was firmly established by Norbert Wiener, McCulloch, Arturo Rosenblueth and others, such as W. Ross Ashby, mathematician Alan Turing, and W. Grey Walter (one of the first to build autonomous robots as an aid to the study of animal behaviour). In the spring of 1947, Wiener was invited to a congress on harmonic analysis, held in Nancy (France was an important geographical locus of early cybernetics together with the US and UK); the event was organized by the Bourbaki, a French scientific society, and mathematician Szolem Mandelbrojt (1899–1983), uncle of the world-famous mathematician Benoît Mandelbrot. During this stay in France, Wiener received the offer to write a manuscript on the unifying character of this part of applied mathematics, which is found in the study of Brownian motion and in telecommunication engineering. The following summer, back in the United States, Wiener decided to introduce the neologism cybernetics, coined to denote the study of "teleological mechanisms", into his scientific theory: it was popularized through his book Cybernetics: Or Control and Communication in the Animal and the Machine (MIT Press/John Wiley and Sons, NY, 1948). In the UK this became the focus for the Ratio Club.

John von Neumann

In the early 1940s John von Neumann contributed a unique and unusual addition to the world of cybernetics: von Neumann cellular automata, and their logical follow up, the von Neumann Universal Constructor. The result of these deceptively simple thought-experiments was the concept of self replication, which cybernetics adopted as a core concept. The concept that the same properties of genetic reproduction applied to social memes, living cells, and even computer viruses is further proof of the somewhat surprising universality of cybernetic study. 

In 1950, Wiener popularized the social implications of cybernetics, drawing analogies between automatic systems (such as a regulated steam engine) and human institutions in his best-selling The Human Use of Human Beings: Cybernetics and Society (Houghton-Mifflin).

Cybernetics in the Soviet Union was initially considered a "pseudoscience" and "ideological weapon" of "imperialist reactionaries" (Soviet Philosophical Dictionary, 1954) and later criticised as a narrow form of cybernetics. In the mid to late 1950s Viktor Glushkov and others salvaged the reputation of the field. Soviet cybernetics incorporated much of what became known as computer science in the West.

While not the only instance of a research organization focused on cybernetics, the Biological Computer Lab at the University of Illinois at Urbana–Champaign, under the direction of Heinz von Foerster, was a major center of cybernetic research for almost 20 years, beginning in 1958.

Split from artificial intelligence

Artificial intelligence (AI) was founded as a distinct discipline at the Dartmouth workshop. After some uneasy coexistence, AI gained funding and prominence. Consequently, cybernetic sciences such as the study of artificial neural networks were downplayed; the discipline shifted into the world of social sciences and therapy.

Prominent cyberneticians during this period include Gregory Bateson and Aksel Berg. 

New cybernetics

In the 1970s, new cyberneticians emerged in multiple fields, but especially in biology. The ideas of Maturana, Varela and Atlan, according to Jean-Pierre Dupuy (1986) "realized that the cybernetic metaphors of the program upon which molecular biology had been based rendered a conception of the autonomy of the living being impossible. Consequently, these thinkers were led to invent a new cybernetics, one more suited to the organizations which mankind discovers in nature - organizations he has not himself invented". However, during the 1980s the question of whether the features of this new cybernetics could be applied to social forms of organization remained open to debate.

In political science, Project Cybersyn attempted to introduce a cybernetically controlled economy during the early 1970s. In the 1980s, according to Harries-Jones (1988) "unlike its predecessor, the new cybernetics concerns itself with the interaction of autonomous political actors and subgroups, and the practical and reflexive consciousness of the subjects who produce and reproduce the structure of a political community. A dominant consideration is that of recursiveness, or self-reference of political action both with regards to the expression of political consciousness and with the ways in which systems build upon themselves".

One characteristic of the emerging new cybernetics considered in that time by Felix Geyer and Hans van der Zouwen, according to Bailey (1994), was "that it views information as constructed and reconstructed by an individual interacting with the environment. This provides an epistemological foundation of science, by viewing it as observer-dependent. Another characteristic of the new cybernetics is its contribution towards bridging the micro-macro gap. That is, it links the individual with the society". Another characteristic noted was the "transition from classical cybernetics to the new cybernetics [that] involves a transition from classical problems to new problems. These shifts in thinking involve, among others, (a) a change from emphasis on the system being steered to the system doing the steering, and the factor which guides the steering decisions; and (b) new emphasis on communication between several systems which are trying to steer each other".

Recent endeavors into the true focus of cybernetics, systems of control and emergent behavior, by such related fields as game theory (the analysis of group interaction), systems of feedback in evolution, and metamaterials (the study of materials with properties beyond the Newtonian properties of their constituent atoms), have led to a revived interest in this increasingly relevant field.

Cybernetics and economic systems

The design of self-regulating control systems for a real-time planned economy was explored by economist Oskar Lange, cyberneticist Viktor Glushkov, and others Soviet cyberneticists during the 1960s. By the time information technology was developed enough to enable feasible economic planning based on computers, the Soviet Union and eastern bloc countries began moving away from planning and eventually collapsed. 

More recent proposals for socialism involve "New Socialism", outlined by the computer scientists Paul Cockshott and Allin Cottrell, where computers determine and manage the flows and allocation of resources among socially owned enterprises.

On the other hand, Friedrich Hayek also mentions cybernetics as a discipline that could help economists understand the "self-organizing or self-generating systems" called markets. Being a "complex phenomena", the best way to examine the market functioning is by using the feedback mechanism, explained by cybernetic theorists. That way, economists could make "pattern predictions".

Therefore, the market for Hayek is a "communication system", an "efficient mechanism for digesting dispersed information". The economist and a cyberneticist are like garderners who are "providing the appropriate environment". Hayek's definition of information is idiosyncratic and precedes the information theory used in cybernetics and the natural sciences.

Finally, Hayek also considers Adam Smith's idea of the invisible hand as an anticipation of the operation of the feedback mechanism in cybernetics. In the same book, Law, Legislation and Liberty, Hayek mentions, along with cybernetics, that economists should rely on the scientific findings of Ludwig von Bertalanffy general systems theory, along with information and communication theory and semiotics.

Subdivisions of the field

Cybernetics is sometimes used as a generic term, which serves as an umbrella for many systems-related scientific fields. 

Basic cybernetics

ASIMO uses sensors and sophisticated algorithms to avoid obstacles and navigate stairs.

Cybernetics studies systems of control as a concept, attempting to discover the basic principles underlying such things as

• Artificial intelligence
• Computer vision
• Control systems
• Conversation theory
• Emergence
• Interactions of actors theory
• Learning organization
• Robotics
• Second-order cybernetics
• Self-organization in cybernetics

In biology

Cybernetics in biology is the study of cybernetic systems present in biological organisms, primarily focusing on how animals adapt to their environment, and how information in the form of genes is passed from generation to generation. There is also a secondary focus on combining artificial systems with biological systems. A notable application to the biology world would be that, in 1955, the physicist George Gamow published a prescient article in Scientific American called "Information transfer in the living cell", and cybernetics gave biologists Jacques Monod and François Jacob a language for formulating their early theory of gene regulatory networks in the 1960s.

• Autopoiesis
• Biocybernetics
• Bioengineering
• Bionics
• Ecology
• Heterostasis
• Homeostasis
• Medical cybernetics
• Neuroscience
• Synthetic biology
• Systems biology
• Practopoiesis

In computer science

Computer science directly applies the concepts of cybernetics to the control of devices and the analysis of information.

• Cellular automaton
• Decision support systems
• Design patterns
• Robotics
• Simulation
• Formal languages
• Modal logic

In engineering

Cybernetics in engineering is used to analyze cascading failures and system accidents, in which the small errors and imperfections in a system can generate disasters. Other topics studied include:

An artificial heart, a product of biomedical engineering.

• Adaptive systems
• Biomedical engineering
• Engineering cybernetics
• Ergonomics
• Systems engineering

In management

• Autonomous agency theory
• Entrepreneurial cybernetics
• Management cybernetics
• Operations research
• Organizational cybernetics
• Systems engineering
• Viable system theory

In mathematics

Mathematical Cybernetics focuses on the factors of information, interaction of parts in systems, and the structure of systems.

• Control theory
• Dynamical system
• Information theory
• Systems theory
• Category theory

 

In psychology

• Attachment theory
• Behavioral cybernetics
• Cognitive psychology
• Consciousness
• Embodied cognition
• Human-robot interaction
• Mind-body problem
• Perceptual control theory
• Psychovector analysis
• Systems psychology

In sociology

By examining group behavior through the lens of cybernetics, sociologists can seek the reasons for such spontaneous events as smart mobs and riots, as well as how communities develop rules such as etiquette by consensus without formal discussion. Affect Control Theory explains role behavior, emotions, and labeling theory in terms of homeostatic maintenance of sentiments associated with cultural categories. The most comprehensive attempt ever made in the social sciences to increase cybernetics in a generalized theory of society was made by Talcott Parsons. In this way, cybernetics establishes the basic hierarchy in Parsons' AGIL paradigm, which is the ordering system-dimension of his action theory. These and other cybernetic models in sociology are reviewed in a book edited by McClelland and Fararo.

• Affect control theory
• Memetics
• Sociocybernetics
• Niklas Luhmann

In art

Nicolas Schöffer's CYSP I (1956) was perhaps the first artwork to explicitly employ cybernetic principles (CYSP is an acronym that joins the first two letters of the words "CYbernetic" and "SPatiodynamic"). The prominent and influential Cybernetic Serendipity exhibition was held at the Institute of Contemporary Arts in 1968 curated by Jasia Reichardt, including Schöffer's CYSP I and Gordon Pask's Colloquy of Mobiles installation. Pask's reflections on Colloquy connected it to his earlier Musicolour installation and to what he termed "aesthetically potent environments", a concept that connected this artistic work to his concerns with teaching and learning. The artist Roy Ascott elaborated an extensive theory of cybernetic art in "Behaviourist Art and the Cybernetic Vision" (Cybernetica, Journal of the International Association for Cybernetics (Namur), Volume IX, No.4, 1966; Volume X No.1, 1967) and in "The Cybernetic Stance: My Process and Purpose" (Leonardo Vol 1, No 2, 1968). Art historian Edward A. Shanken has written about the history of art and cybernetics in essays including "Cybernetics and Art: Cultural Convergence in the 1960s" and From Cybernetics to Telematics: The Art, Pedagogy, and Theory of Roy Ascott (2003), which traces the trajectory of Ascott's work from cybernetic art to telematic art (art using computer networking as its medium, a precursor to net.art.)

• Telematic art
• Interactive art
• Systems art

In architecture and design

Cybernetics was an influence on thinking in architecture and design in the decades after the Second World War. Ashby and Pask were drawn on by design theorists such as Horst Rittel, Christopher Alexander and Bruce Archer. Pask was a consultant to Nicholas Negroponte's Architecture Machine Group, forerunner of the MIT Media Lab, and collaborated with architect Cedric Price and theatre director Joan Littlewood on the influential Fun Palace project during the 1960s. Pask's 1950s Musicolour installation was the inspiration for John and Julia Frazer's work on Price's Generator project. There has been a resurgence of interest in cybernetics and systems thinking amongst designers in recent decades, in relation to developments in technology and increasingly complex design challenges. Figures such as Klaus Krippendorff, Paul Pangaro and Ranulph Glanville have made significant contributions to both cybernetics and design research. The connections between the two fields have come to be understood less in terms of application and more as reflections of each other.

In Earth system science

Geocybernetics aims to study and control the complex co-evolution of ecosphere and anthroposphere, for example, for dealing with planetary problems such as anthropogenic global warming. Geocybernetics applies a dynamical systems perspective to Earth system analysis. It provides a theoretical framework for studying the implications of following different sustainability paradigms on co-evolutionary trajectories of the planetary socio-ecological system to reveal attractors in this system, their stability, resilience and reachability. Concepts such as tipping points in the climate system, planetary boundaries, the safe operating space for humanity and proposals for manipulating Earth system dynamics on a global scale such as geoengineering have been framed in the language of geocybernetic Earth system analysis.

In sport

A model of cybernetics in Sport was introduced by Yuri Verkhoshansky and Mel C. Siff in 1999 in their book Supertraining.

In law

As a form of regulation, cybernetics has been always close to law, specially in regulation and legal sciences, through the next topics:

• Organizations and superorganisms
• Ontology, logic and artificial intelligence
• Complex adaptive systems
• Smart contracts
• Control systems
• Self-organization in cybernetics
• Cyberethics
• Regulation
• Consensus systems
• Metagovernment

Related fields

Complexity science

Complexity science attempts to understand the nature of complex systems. 

Aspects of complexity science include:

• Complex adaptive system
• Complex systems
• Complexity theory
• Decision intelligence

Biomechatronics

Biomechatronics relates to linking mechatronics to biological organisms, leading to systems that conform to A. N. Kolmogorov's definition of Cybernetics: "Science concerned with the study of systems of any nature which are capable of receiving, storing and processing information so as to use it for control". From this perspective mechatronics are considered technical cybernetics or engineering cybernetics.

Growth cone

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Growth_cone

 

Image of a fluorescently-labeled growth cone extending from an axon F-actin (red) microtubules (green).

A growth cone is a large actin-supported extension of a developing or regenerating neurite seeking its synaptic target. Their existence was originally proposed by Spanish histologist Santiago Ramón y Cajal based upon stationary images he observed under the microscope. He first described the growth cone based on fixed cells as "a concentration of protoplasm of conical form, endowed with amoeboid movements" (Cajal, 1890). Growth cones are situated on the tips of neurites, either dendrites or axons, of the nerve cell. The sensory, motor, integrative, and adaptive functions of growing axons and dendrites are all contained within this specialized structure. 

Structure

Two fluorescently-labeled growth cones. The growth cone (green) on the left is an example of a “filopodial” growth cone, while the one on the right is a “lamellipodial” growth cone. Typically, growth cones have both structures, but with varying sizes and numbers of each.

The morphology of the growth cone can be easily described by using the hand as an analogy. The fine extensions of the growth cone are pointed filopodia known as microspikes. The filopodia are like the "fingers" of the growth cone; they contain bundles of actin filaments (F-actin) that give them shape and support. Filopodia are the dominant structures in growth cones, and they appear as narrow cylindrical extensions which can extend several micrometres beyond the edge of the growth cone. The filopodia are bound by a membrane which contains receptors, and cell adhesion molecules that are important for axon growth and guidance. 

In between filopodia—much like the webbing of the hands—are the "lamellipodia". These are flat regions of dense actin meshwork instead of bundled F-actin as in filopodia. They often appear adjacent to the leading edge of the growth cone and are positioned between two filopodia, giving them a "veil-like" appearance. In growth cones, new filopodia usually emerge from these inter-filopodial veils.

The growth cone is described in terms of three regions: the peripheral (P) domain, the transitional (T) domain, and the central (C) domain. The peripheral domain is the thin region surrounding the outer edge of the growth cone. It is composed primarily of an actin-based cytoskeleton, and contains the lamellipodia and filopodia which are highly dynamic. Microtubules, however, are known to transiently enter the peripheral region via a process called dynamic instability. The central domain is located in the center of the growth cone nearest to the axon. This region is composed primarily of a microtubule-based cytoskeleton, is generally thicker, and contains many organelles and vesicles of various sizes. The transitional domain is the region located in the thin band between the central and peripheral domains.

Growth cones are molecularly specialized, with transcriptomes and proteomes that are distinct from those of their parent cell bodies.^[3] There are many cytoskeletal-associated proteins, which perform a variety of duties within the growth cone, such as anchoring actin and microtubules to each other, to the membrane, and to other cytoskeletal components. Some of these components include molecular motors that generate force within the growth cone and membrane-bound vesicles which are transported in and out of the growth cone via microtubules. Some examples of cytoskeletal-associated proteins are Fascin and Filamin (actin bundling), Talin (actin anchoring), myosin (vesicle transport), and mDia (microtubule-actin linking). 

Axon branching and outgrowth

The highly dynamic nature of growth cones allows them to respond to the surrounding environment by rapidly changing direction and branching in response to various stimuli. There are three stages of axon outgrowth, which are termed: protrusion, engorgement, and consolidation. During protrusion, there is a rapid extension of filopodia and lamellar extensions along the leading edge of the growth cone. Engorgement follows when the filopodia move to the lateral edges of the growth cone, and microtubules invade further into the growth cone, bringing vesicles and organelles such as mitochondria and endoplasmic reticulum. Finally, consolidation occurs when the F-actin at the neck of the growth cone depolymerizes and the filopodia retract. The membrane then shrinks to form a cylindrical axon shaft around the bundle of microtubules. One form of axon branching also occurs via the same process, except that the growth cone “splits” during the engorgement phase. This results in the bifurcation of the main axon. An additional form of axon branching is termed collateral (or interstitial) branching. Collateral branching, unlike axon bifurcations, involves the formation of a new branch from the established axon shaft and is independent of the growth cone at the tip of the growing axon. In this mechanism, the axon initially generates a filopodium or lamellipodium which following invasion by axonal microtubules can then develop further into a branch extending perpendicular from the axon shaft. Established collateral branches, like the main axon, exhibit a growth cone and develop independently of the main axon tip. 

Overall, axon elongation is the product of a process known as tip growth. In this process, new material is added at the growth cone while the remainder of the axonal cytoskeleton remains stationary. This occurs via two processes: cytoskeletal-based dynamics and mechanical tension. With cytoskeletal dynamics, microtubules polymerize into the growth cone and deliver vital components. Mechanical tension occurs when the membrane is stretched due to force generation by molecular motors in the growth cone and strong adhesions to the substrate along the axon. In general, rapidly growing growth cones are small and have a large degree of stretching, while slow moving or paused growth cones are very large and have a low degree of stretching. 

The growth cones are continually being built up through construction of the actin microfilaments and extension of the plasma membrane via vesicle fusion. The actin filaments depolymerize and disassemble on the proximal end to allow free monomers to migrate to the leading edge (distal end) of the actin filament where it can polymerize and thus reattach. Actin filaments are also constantly being transported away from the leading edge by a myosin-motor driven process known as retrograde F-actin flow. The actin filaments are polymerized in the peripheral region and then transported backward to the transitional region, where the filaments are depolymerized; thus freeing the monomers to repeat the cycle. This is different from actin treadmilling since the entire protein moves. If the protein were to simply treadmill, the monomers would depolymerize from one end and polymerize onto the other while the protein itself does not move. 

The growth capacity of the axons lies in the microtubules which are located just beyond the actin filaments. Microtubules can rapidly polymerize into and thus “probe” the actin-rich peripheral region of the growth cone. When this happens, the polymerizing ends of microtubules come into contact with F-actin adhesion sites, where microtubule tip-associated proteins act as "ligands". Laminins of the basal membrane interact with the integrins of the growth cone to promote the forward movement of the growth cone. Additionally, axon outgrowth is also supported by the stabilization of the proximal ends of microtubules, which provide the structural support for the axon. 

Axon guidance

Model of growth cone-mediated axon guidance. From left to right, this model describes how the cytoskeleton responds and reorganizes to grow towards a positive stimulus (+) detected by receptors in the growth cone or away from a negative stimulus (-).

 

Movement of the axons is controlled by an integration of its sensory and motor function (described above) which is established through second messengers such as calcium and cyclic nucleotides. The sensory function of axons is dependent on cues from the extracellular matrix which can be either attractive or repulsive, thus helping to guide the axon away from certain paths and attracting them to their proper target destinations. Attractive cues inhibit retrograde flow of the actin filaments and promote their assembly whereas repulsive cues have the exact opposite effect. Actin stabilizing proteins are also involved and are essential for continued protrusion of filopodia and lamellipodia in the presence of attractive cues, while actin destabilizing proteins are involved in the presence of a repulsive cue.

A similar process is involved with microtubules. In the presence of an attractive cue on one side of the growth cone, specific microtubules are targeted on that side by microtubule stabilizing proteins, resulting in growth cone turning in the direction of the positive stimulus. With repulsive cues, the opposite is true: microtubule stabilization is favored on the opposite side of the growth cone as the negative stimulus resulting in the growth cone turning away from the repellent. This process coupled with actin-associated processes result in the overall directed growth of an axon.

Growth cone receptors detect the presence of axon guidance molecules such as Netrin, Slit, Ephrins, and Semaphorins. It has more recently been shown that cell fate determinants such as Wnt or Shh can also act as guidance cues. The same guidance cue can act as an attractant or a repellent, depending on context. A prime example of this is Netrin-1, which signals attraction through the DCC receptor and repulsion through the Unc-5 receptor. Furthermore, it has been discovered that these same molecules are involved in guiding vessel growth. Axon guidance directs the initial wiring of the nervous system and is also important in axonal regeneration following an injury.