Under quantum biological

law there can be free energy

This accounts for breatharians and many other factors

http://medicalexposedownloads.com/PDF/Creatures%20who%20can%20live%20on%20Energy.pdf

http://indavideo.hu/video/Breatharian_Boy

Something from nothing is a quantum possibility. Werner Heisenberg’s Uncertainty Principle opened the doors to overturning the law of energy conservation. 2009-01-14 Something from nothing is a quantum possibility

[The photo of Werner Heisenberg is taken from http://www.aprender-mat.info/historyDetail.htm?id=Heisenberg]

Is it ever possible to get something for nothing? The global wave of financial scandals has been widely seen as

confirmation that “only nothing can come from nothing”, as the Greek philosopher Parmenides argued around 2,500

years ago and finger-wagging moralists have been telling us ever since.

Slackers everywhere should therefore take heart from the mounting evidence that Parmenides and his ilk could not

have been more wrong. It is now becoming clear that everything can – and probably did – come from nothing.

Whenever some common-sense view of the nature of reality is challenged like this, you can bet quantum theory will

be involved. And so it proves in this case, with two recent advances in the understanding of the subatomic world

adding to the weight of evidence.

Unlike financial scam artists, physicists have been amassing evidence for their unlikely claim for decades, beginning

with the discovery by a young German theoretician of a loophole in a supposedly inviolable law of nature.

As countless generations of schoolchildren are taught to parrot in class, the law of conservation of energy states that

it cannot be created or destroyed, but merely transformed from one form to another.

In 1927, Dr Werner Heisenberg showed that the truth is rather more interesting in a paper that addressed a

philosophical question: how do we know what reality is like? The answer seems obvious: by making observations. But

Dr Heisenberg pointed out that the newly emerging quantum theory implied that the very act of observation affects

whatever is being observed. That, in turn, means it is impossible to know with total precision what reality is actually

like.

Dr Heisenberg went on to show that his now-celebrated Uncertainty Principle implies there is always some uncertainty

about properties of any region of space – specifically, how much energy it contains over a given period. The “law” of

energy conservation is thus merely a conceit, and one whose violation leads to some astonishing consequences –

including support for the something-for-nothing view of reality.

Heisenberg’s principle implies, for example, that the very space around us is seething with subatomic particles,

popping in and out of empty space. During their fleeting existence, these “vacuum particles” interact with each other,

and turn the supposedly dull vacuum of space into the quantum vacuum – which astronomers now know is anything

but dull. Observations suggest the expansion of the entire cosmos is being propelled by quantum vacuum energy, in

the form of enigmatic “dark energy”.

Something for nothing can also be seen working its magic down at the other scale of things. In the late 1940s, the

Dutch physicist Hendrik Casimir predicted that the quantum vacuum could generate a force-field between two flat

plates of metal. This “Casimir Effect” again emerges literally out of nowhere, pushing the plates together.

The force is pretty feeble: between two book-sized plates separated by just a hair’s breadth, it is equivalent to barely

the weight of the ink in this sentence’s full stop, and it was properly measured only in the mid-1990s. Even so, it’s

enough to cause the components of delicate micro-mechanical devices to seize up.

Fortunately, back in the 1960s some Soviet theorists predicted that the quantum vacuum can be engineered so that

the Casimir force becomes one of repulsion rather than attraction. And last week a team of scientists in the US

reported in the journal Nature that they had confirmed the prediction in dramatic style, using the repulsive form of the

force to levitate a gold-plated ball. OK, the ball was less than the size of a full stop, but that’s pretty impressive

considering it was being held aloft by nothing but the energy of empty space.

Some theorists now think they can go even further, and use the physics of something for nothing to explain the origin

of literally everything. They claim that the Big Bang from which the entire universe emerged was the result of

convulsions in the quantum vacuum which took place around 14 billion years ago.

New theoretical work on the nature of matter suggests we may now have to regard even ourselves to be

manifestations of the quantum vacuum.

All atoms are made up of electrons plus a far more massive central nucleus, made up of clusters of particles called

quarks. It seems obvious that the mass of the nucleus must be the sum total of the masses of its quarks – but that

reckons without the effect of the quantum vacuum. It turns out that the quarks account for only a tiny fraction of the

total mass of a nucleus. By far the bulk comes from the subatomic “glue” that binds its quarks together. And this glue

takes the form of vacuum particles flitting in and out of existence.

That at least is the theory. Confirming it requires some appallingly difficult calculations, involving all the different

manifestations of quantum vacuum particles inside the nucleus – of which there are trillions. At the John von

Neumann Institute for Computing in Jülich, Germany, Dr Stephan Dürr and colleagues have had a shot at doing this

titanic calculation, using a computer capable of performing over 100 million million calculations a second.

After several months of number-crunching, the machine has now spat out its estimate for the mass of a hydrogen

nucleus, and it is within 2 per cent of the value measured in the lab. In other words, virtually all the mass contained in

atoms – and indeed us – appears to be nothing more than the evanescent energy of empty space.

“virtually all the mass contained in atoms –

and indeed us – appears to be nothing more

than the evanescent energy of empty space.”

It thus seems that much as we may like to distance ourselves from financial scam artists and get-rich-quick schemes,

we are all living proof that it’s possible to get something for nothing.

Robert Matthews is Visiting Reader in Science at Aston University, Birmingham, England

Free Bio-energy principle

The free bio-energy principle tries to explain how (biological) systems maintain their order (non-equilibrium steady-state) by restricting themselves to a limited number of states.[1] It says that biological systems minimise a free energy functional of their internal states, which entail beliefs about hidden states in their environment. The implicit minimisation of variational free energy is formally related to variational Bayesian methods and was originally introduced by Karl Friston as an explanation for embodied perception inneuroscience,[2] where it is also known as active inference.

Background

The notion that self-organising biological systems – like a cell or brain – can be understood as minimising variational free energy is based upon Helmholtz’s observations on unconscious inference[3] and subsequent treatments in psychology [4] and machine learning.[5] Variational free energy is a functional of some outcomes and a probability density over their (hidden) causes. This variational density is defined in relation to a probabilistic model that generates outcomes from causes. In this setting, free energy provides an (upper bound) approximation to Bayesian model evidence.[6] Its minimisation can therefore be used to explain Bayesian inference and learning. When a system actively samples outcomes to minimise free energy, it implicitly performs active inference and maximises the evidence for its (generative) model.

However, free energy is also an upper bound on the self-information (or surprise) of outcomes, where the long-term average of surprise is entropy. This means that if a system acts to minimise free energy, it will implicitly place an upper bound on the entropy of the outcomes – or sensory states – it samples.[7]

Relationship to other theories

Active inference is closely related to the good regulator theorem [8] and related accounts of self-organisation,[9][10] such as self-assembly, pattern formation and autopoiesis.[11] It addresses the themes considered in cybernetics, synergetics [12] and embodied cognition. Because free energy can be expressed as the expected energy (of outcomes) under the variational density minus its entropy, it is also related to the maximum entropy principle.[13] Finally, because the time average of energy is action, the principle of minimum variational free energy is a principle of least action.

Free energy landscape of the DNA molecule

Definition

Definition (continuous formulation): Active inference rests on the tuple ,

A sample space – from which random fluctuations are drawn

Hidden or external states – that cause sensory states and depend on

action

Sensory states – a probabilistic mapping from action and hidden

states

Action – that depends on sensory and internal states

Internal states – that cause action and depend on sensory states

Generative density – over sensory and hidden states under a generative model

Variational density – over hidden states that is parameterised by internal

states

Action and perception

The objective is to maximise model evidence or minimise surprise . This generally involves an intractable marginalisation over hidden states, so surprise is replaced with an upper variational free energy bound.[5] However, this means that internal states must also minimise free energy, because free energy is a functional of sensory and internal states:

This induces a dual minimisation with respect to action and internal states that correspond to action and perception respectively.

Free energy minimisation

Free energy minimisation and self-organisation

Free energy minimisation has been proposed as a hallmark of self-organising systems, when cast as random dynamical systems.[14] This formulation rests on a Markov blanket (comprising action and sensory states) that separates internal and external states. If internal states and action minimise free energy, then they place an upper bound on the entropy of sensory states

This is because – under ergodic assumptions – the long-term average of surprise is entropy. This bound resists a natural tendency to disorder – of the sort associated with the second law of thermodynamics and the fluctuation theorem.

Free energy minimisation and Bayesian inference

All Bayesian inference can be cast in terms of free energy minimisation; e.g.,.[15] When free energy is minimised with respect to internal states, the Kullback–Leibler divergence between the variational and posterior density over hidden states is minimised. This corresponds to approximate Bayesian inference – when the form of the variational density is fixed – and exact Bayesian inference otherwise. Free energy minimisation therefore provides a generic description of Bayesian inference and filtering (e.g., Kalman filtering). It is also used in Bayesian model selection, where free energy can be usefully decomposed into complexity and accuracy:

Models with minimum free energy provide an accurate explanation of data, under complexity costs (c.f., Occam's razor and more formal treatments of computational costs [16]). Here, complexity is the divergence between the variational density and prior beliefs about hidden states (i.e., the effective degrees of freedom used to explain the data).

Free energy minimisation and thermodynamics

Variational free energy is an information theoretic functional and is distinct from thermodynamic (Helmholtz) free energy.[17] However, the complexity term of variational free energy shares the same fixed point as Helmholtz free energy (under the assumption the system is thermodynamically closed but not isolated). This is because if sensory perturbations are suspended (for a suitably long period of time), complexity is minimised (because accuracy can be neglected). At this point, the system is at equilibrium and internal states minimise Helmholtz free energy, by the principle of minimum energy.[18]

Free energy minimisation and information theory

Free energy minimisation is equivalent to maximising the mutual information between sensory states and internal states that parameterise the variational density (for a fixed entropy variational density).[7] This relates free energy minimization to the principle of minimum redundancy [19] and related treatments using information theory to describe optimal behaviour.[20][21]

Free energy minimisation in neuroscience

Free energy minimisation provides a useful way to formulate normative (Bayes optimal) models of neuronal inference and learning under uncertainty [22] and therefore subscribes to the Bayesian brain hypothesis.[23] The neuronal processes described by free energy minimisation

depend on the nature of hidden states: that can comprise time dependent variables, time invariant parameters and the precision (inverse variance or temperature) of random fluctuations. Minimising variables, parameters and precision corresponds to inference, learning and the encoding of uncertainty, respectively:

Perceptual inference and categorisation

Free energy minimisation formalises the notion of unconscious inference in perception [3][5] and provides a normative (Bayesian) theory of neuronal processing. The associated process theory of neuronal dynamics is based on minimising free energy through gradient descent. This corresponds to generalised Bayesian filtering (where ~ denotes a variable in generalised coordinates of motion

and is a derivative matrix operator):[24]

Usually, the generative models that define free energy are non-linear and hierarchical (like cortical hierarchies in the brain). Special cases of generalised filtering include Kalman filtering, which is formally equivalent to predictive coding [25] – a popular metaphor for message passing in the brain. Under hierarchical models, predictive coding involves the recurrent exchange of ascending (bottom-up) prediction errors and descending (top-down) predictions [26] that is consistent with the anatomy and physiology of sensory [27] and motor systems.[28]

Perceptual learning and memory

In predictive coding, optimising model parameters through a gradient ascent on the time integral of free energy (free action) reduces to associative or Hebbian plasticity and is associated with synaptic plasticity in the brain.

Perceptual precision, attention and salience

Optimising the precision parameters corresponds to optimising the gain of prediction errors (c.f., Kalman gain). In neuronally plausible implementations of predictive coding,[26] this corresponds to optimising the excitability superficial pyramidal cells and has been interpreted in terms of attentional gain.[29]

Active inference

When gradient descent is applied to action , motor control can be understood in terms of classical reflex arcs that are engaged by descending (corticospinal) predictions. This provides a formalism that generalizes the equilibrium point solution – to the degrees of freedom problem [30] – to movement trajectories.

Active inference and optimal control

Active inference is related to optimal control by replacing value or cost-to-go functions with prior beliefs about state transitions or flow.[31] This exploits the close connection between Bayesian filtering and the solution to the Bellman equation. However, active inference starts with (priors

over) flow that are specified with scalar and vector

value functions of state space (c.f., the Helmholtz decomposition). Here, is the amplitude of

random fluctuations and cost is . The priors over

flow induce a prior over states that is the solution to the appropriate forward Kolmogorov equations.[32] In contrast, optimal control optimises the flow,

given a cost function, under the assumption that (i.e., the flow is curl free or has detailed balance). Usually, this entails solving backward Kolmogorov equations.[33]

Active inference and optimal decision (game) theory

Optimal decision problems (usually formulated as partially observable Markov decision processes) are treated within active inference by absorbing utility functions into prior beliefs. In this setting, states that have a high utility (low cost) are states an agent expects to occupy. By equipping the generative model with hidden states that model control, policies (control sequences) that minimise variational free energy lead to high utility states.[34]

Neurobiologically, neuromodulators like dopamine are considered to report the precision of prediction errors by modulating the gain of principal cells encoding prediction error.[35] This is closely related to – but formally distinct from – the role of dopamine in reporting prediction errors per se [36] and related computational accounts.[37]

Active inference and cognitive neuroscience

Active inference has been used to address a range of issues in cognitive neuroscience, brain function and neuropsychiatry, including: action observation,[38] mirror neurons,[39] saccades and visual search,[40] sleep,[41] illusions,[42] attention,[29] action section,[35]hysteria [43] and psychosis.[44]

References

1. Ashby, W. R. (1962). Principles of the self-organizing system.in Principles of Self-

Organization: Transactions of the University of Illinois Symposium, H. Von Foerster and G. W. Zopf, Jr.

(eds.), Pergamon Press: London, UK, pp. 255–278.

2. Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. J Physiol

Paris. , 100 (1–3), 70–87.

3. ^ Jump up to:a b Helmholtz, H. (1866/1962). Concerning the perceptions in general. In Treatise

on physiological optics (J. Southall, Trans., 3rd ed., Vol. III). New York: Dover.

4. Gregory, R. L. (1980). Perceptions as hypotheses. Phil Trans R Soc Lond B. , 290, 181–197.

5. ^ Jump up to:a b c Dayan, P., Hinton, G. E., & Neal, R. (1995). The Helmholtz machine. Neural

Computation , 7, 889–904.

6. Beal, M. J. (2003). Variational Algorithms for Approximate Bayesian Inference. PhD. Thesis,

University College London.

7. b Friston, K. (2012). A Free Energy Principle for Biological Systems. Entropy , 14, 2100–2121.

8. Conant, R. C., & Ashby, R. W. (1970). Every Good Regulator of a system must be a model of

that system. Int. J. Systems Sci. , 1 (2), 89–97.

9. Kauffman, S. (1993). The Origins of Order: Self-Organization and Selection in Evolution.

Oxford: Oxford University Press.

10. Nicolis, G., & Prigogine, I. (1977). Self-organization in non-equilibrium systems. New York:

John Wiley.

11. Maturana, H. R., & Varela, F. (1980). Autopoiesis: the organization of the living. In V. F.

Maturana HR (Ed.), Autopoiesis and Cognition. Dordrecht, Netherlands: Reidel.

12. Haken, H. (1983). Synergetics: An introduction. Non-equilibrium phase transition and self-

organisation in physics, chemistry and biology (3rd ed.). Berlin: Springer Verlag.

13. Jaynes, E. T. (1957). Information Theory and Statistical Mechanics. Physical Review Series II ,

106 (4), 620–30.

14. Crauel, H., & Flandoli, F. (1994). Attractors for random dynamical systems. Probab Theory

Relat Fields , 100, 365–393.

15. Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear Gaussian models. Neural

Computat. , 11 (2), 305–45.

16. Ortega, P. A., & Braun, D. A. (2012). Thermodynamics as a theory of decision-making with

information processing costs. Proceedings of the Royal Society A, vol. 469, no. 2153 (20120683) .

17. Evans, D. J. (2003). A non-equilibrium free energy theorem for deterministic systems.

Molecular Physics , 101, 15551–4.

18. Jarzynski, C. (1997). Nonequilibrium equality for free energy differences. Phys. Rev. Lett., 78,

2690.

19. Barlow, H. (1961). Possible principles underlying the transformations of sensory messages. In

W. Rosenblith (Ed.), Sensory Communication (pp. 217-34). Cambridge, MA: MIT Press.

20. Linsker, R. (1990). Perceptual neural organization: some approaches based on network

models and information theory. Annu Rev Neurosci. , 13, 257–81.

21. Bialek, W., Nemenman, I., & Tishby, N. (2001). Predictability, complexity, and learning. Neural

Computat., 13 (11), 2409–63.

22. Friston, K. (2010). The free-energy principle: a unified brain theory? Nat Rev Neurosci. , 11

(2), 127–38.

23. Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding

and computation. Trends Neurosci. , 27 (12), 712–9.

24. Friston, K., Stephan, K., Li, B., & Daunizeau, J. (2010). Generalised Filtering. Mathematical

Problems in Engineering , vol., 2010, 621670

25. Rao, R. P., & Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional

interpretation of some extra-classical receptive-field effects. Nat Neurosci. , 2 (1), 79–87.

26. b Mumford, D. (1992). On the computational architecture of the neocortex. II. Biol. Cybern. ,

66, 241–51.

27. Bastos, A. M., Usrey, W. M., Adams, R. A., Mangun, G. R., Fries, P., & Friston, K. J.

(2012). Canonical microcircuits for predictive coding. Neuron , 76 (4), 695–711.

28. Adams, R. A., Shipp, S., & Friston, K. J. (2013). Predictions not commands: active inference in

the motor system. Brain Struct Funct. , 218 (3), 611–43

29. b Feldman, H., & Friston, K. J. (2010). Attention, uncertainty, and free-energy. Frontiers in

Human Neuroscience , 4, 215.

30. Feldman, A. G., & Levin, M. F. (1995). The origin and use of positional frames of reference in

motor control. Behav Brain Sci. , 18, 723–806.

31. Friston, K., (2011). What is optimal about motor control?. Neuron, 72(3), 488–98.

32. Friston, K., & Ao, P. (2012). Free-energy, value and attractors. Computational and

mathematical methods in medicine, 2012, 937860.

33. Kappen, H., (2005). Path integrals and symmetry breaking for optimal control theory. Journal

of Statistical Mechanics: Theory and Experiment, 11, p. P11011.

34. Friston, K., Samothrakis, S. & Montague, R., (2012). Active inference and agency: optimal

control without cost functions. Biol. Cybernetics, 106(8–9), 523–41.

35. b Friston, K. J. Shiner T, FitzGerald T, Galea JM, Adams R, Brown H, Dolan RJ, Moran R,

Stephan KE, Bestmann S. (2012). Dopamine, affordance and active inference. PLoS Comput Biol., 8(1), p.

e1002327.

36. Fiorillo, C. D., Tobler, P. N. & Schultz, W., (2003). Discrete coding of reward probability and

uncertainty by dopamine neurons. Science, 299(5614), 1898–902.

37. Frank, M. J., (2005). Dynamic dopamine modulation in the basal ganglia: a

neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism. J Cogn

Neurosci., Jan, 1, 51–72.

38. Friston, K., Mattout, J. & Kilner, J., (2011). Action understanding and active inference. Biol

Cybern., 104, 137–160.

39. Kilner, J. M., Friston, K. J. & Frith, C. D., (2007). Predictive coding: an account of the mirror

neuron system. Cogn Process., 8(3), pp. 159–66.

40. Friston, K., Adams, R. A., Perrinet, L. & Breakspear, M., (2012). Perceptions as hypotheses:

saccades as experiments. Front Psychol., 3, 151.

41. Hobson, J. A. & Friston, K. J., (2012). Waking and dreaming consciousness: Neurobiological

and functional considerations. Prog Neurobiol, 98(1), pp. 82–98.

42. Brown, H., & Friston, K. J. (2012). Free-energy and illusions: the cornsweet effect. Front

Psychol , 3, 43.

43. Edwards, M. J., Adams, R. A., Brown, H., Pareés, I., & Friston, K. J. (2012). A Bayesian

account of 'hysteria'. Brain , 135(Pt 11):3495–512.

44. Adams RA, Perrinet LU, Friston K. (2012). Smooth pursuit and visual occlusion: active

inference and