ISSN: 2574-1241 Volume 5- Issue 4: 2018 DOI: 10.26717/BJSTR.2018.06.001398 Robert Skopec. Biomed J Sci & Tech Res

Mini Review Open Access

Brain as a Anti-Entropic Intelligence Generator

Robert Skopec* Researcher-Analyst, Slovakia

Received: June 27, 2018; Published: July 11, 2018 *Corresponding author: : Robert Skopec, Analyst-Researcher, Dubnik, Slovakia

Abstract

There are several spheres of underdevelopment in recent research. Brain research (BR) is too oriented to macro-states findings, ignoring the process of reentry between macro-states and micro-states, especially is not reflecting the macro-states influence on internal representations at micro level (links to quantum physics). There is a tendency to empirical isolationism, unilateral concentration only to the system, not studying equally the mechanisms of interplay between system and the environment. We are underestimating the informational impact of a factor to the brains behavior. Mathematical approach as a pure method of the artificial intelligence is not enough implemented, despite this, we believe with A. Einstein, that the mathematics is a language of God.

Introduction has a set of polar decompositions: Γ=Σkqkϕφ k ⊗ k with the qk The consciousness is a mathematical structure, with

q= () qk k complex. They are parametrized by the right toroid T of amplitudes neurobiological semantics. The higher mental processes all state space of U = V+W. The evolution of q is determined via the and comprise a singular bundle over S, the enlarged correspond to well-defined but, at present, poorly understood information processing functions that are carried out by physical (pp12 , ....) connection on this bundle. Each fieber T (axons, dendrites) has systems, our brains[1].The consciousness is the result of a global a unique natural convex partition yielding the correct workspace (GW) in , which distributes information to Γ , and intersects p j in an arc of ,2 since the circle of unit vectors which generate the the huge number of paralell unconscious processors forming q j . In this interpretation, rays in Hilbert space correspond corresponding to the state of a rest of the brain. The GW is composed of many different paralell to ensembles, and unit vectors in a ray correspond to individual on the symbolic representations that it receives as input [2]. The length processors, or modules, each capable of performing some task Γ above vector Γ in the unit sphere S of Η member plane waves of such ensemble. An element of the fieber moduls are flexible in that they can combine to form new processors component processors [3]. Ρ . This fieber can be capable of performing novel tasks, and can decompose into smaller polar decomposition of Γ at synapse: thought as an information carrier of the set all the possible complex ∑ The Cortex as a Prediction Generator Γ=qk Γ k k where the qk ∈ C , and the Γk are bi – orthonormal. Thus each Γk consist from two modular subsystems: Global workspace system at the neurobiological level may is of the form ϕφkk⊗ where the φk and theϕk are orthonormal [6]. a)

S1 ,composed of a net of parallel, distributed and functionally The first is a processing network of the coputational space workspace S 2 specialised processors (from primary sensory processors The second is the projective modular system of a global – area V1, unimodal processors – area V4, heteromodal , consisting of a set of cortical neurons characterized processors – the resonant mirror neurons in area F5) [4]. by ability to receive from and send back to homologous neurons in other cortical areas horizontal projections through long-range excitatory axons [7]. The populations of neurons are distributed They are processing categorical, semantic information. The among brain areas in variable proportions. It is known that reality is a theory arising from interferrence of computed representation. The interferrence product is therefore “true” long-range cortico-cortical tangential connections, and callosal inputs compared operatinally in GW with a resonant pre- [5]. connections originate from the pyramidal cells of layers 2 and 3, If V is a physical system in interaction with another system W. which give or receive the so called “association“ efferents and afferents [8]. These cortical neurons establish strong vertical and Kochen´s standard state vector Γ(t) reciprocal connections, via layer 5 neurons, with corresponding Biomedical Journal of of the two interacting systems thalamic nuclei, contributing to the direct access to the processing Scientific & Technical Research (BJSTR) 5454 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

input feature X j takes value x is Cc= networks. These projections may selectively amplify, or extinquish interconnected in a way, that only one workspace representation given the value of the class label inputs from processing neurons. The entire workspace is globally [14]. The feature selection filter in the perception can be information between the class variable and each attribute variable used the naïve Bayesian classifier based on the empirical mutual Thecan be Predictor active in a given time [2].

[15]. The mutual information is computedP( X j during= xC, = c) the learning The projective system (PS) is involved in the main computational MI( xjj , c )=∑∑ P ( x = x , C = c ) log process learning. xc PX( j = xPC) ( = c) a a To predict the value of ry r of a particular part of the brain: the predictive metalearning system of has active user a neuromodulator circuits wthin the circuits of basal ganglia, which given the profile For prediction functions are important also the connections of we apply a slightly modifiedV prediction rulea to allow been suggested [9] as major locus of reinforcement learning. a ∂(rwj ,) ry= arg max PR( z= v) PR( jy= wR = v) for missing values. Thev prediction rule is  . j≠= yw1 prefrontal cortex, thalamus, cerebellum, etc. In a computational ödel has pointed out, that predictions are like a perception theory on the roles of the ascending neuromodulatory systems, of the objects of set theory. Prediction is a mode of mathematical Kurt G they are mediating the global signals regulating the distributed learning mechanism in the brain [10]. Dopamine represents the intuition, which in sense of perception induces building up theories global learning signal for prediction of rewards and reinforcement to the abstract elements containd in our empirical ideas. The of the future. The given underlying mathematics is closely related of actions. The reinforcement learning in this computational brain seems to have internal theories about what the world is framework is for an agent to take an action in response to the state like. Between [6] brains theories is a internal perceptual rivalry in of environment to be acquired reward maximised in a long run. Darwinian sense. The World as a quantum system can be described A basic algorithm of reinforcement learning could be St( )∈ ( s1 ,..... sn ) considered a Markov decision problem (MDP) assuming a discrete due the polar decomposition, as a whole system consisting from ,takes action at( )∈ ( a1 ,..... an ) state, action and time. The agent observes the state deterministically as a= Gs(),or stochastically P= () as . In response two subsystems, which are mutually observing one another. according to its policy, given either at() During this observation the global workspace is processing reentry between internal representations and influence functional of the deterministically as st()= Fst ((),()) at j th of elements subset to the agent´s action , the state of environment changes either k Pst( (+ 1) st ( ) , at() for each action at(). environment,k between left and right hemisphere, etc [3]. Some ()X j taken from isolated neural system X, and its complement , or stochastically according authors are proposing to consider a The reward +∈ is deterministically rt(+= 1) Rst ( ( ), at ( )) , or XX− k to a Markov transitionrt( 1) matrix R j stochastically Prt( (+ 1) st ( ) , at( )) and the rest of the system introduce statistical dependence (Edelman, 1998)[R]. Interactions between the subset between the two. This is measured by their mutual information

,[11,12] In this setup, the goal of kk k k MIX(jj , X−= X ) H (( X j ) + HX ( −− X j ) HX ( ) reinforcement learning is to find an optimal policy maximising the k k which the of X j is accounted for by the entropy of XX− j expected sum of future rewards. The architecture for reinforcement ,which captures the extent to to predict future rewards in the form of a state value function Vs() and vice versa (H indicates statistical entropy) [16]. learning is the actor-critic, consisting of two parts: The critic learns for the current policy, The actor improves the policy = P() as Learning . in reference to the future reward predicted by the critic. The actor and the critic learn either alternately, or concurrently. The Signalling between neuronal groups occurs via excitatory synaptic weights between two excitatory units are assumed to connections linking cortical areas, usually in a reciprocal fashion. ∆=wpost, pre ε RSpre(2 S Post − 1) be modifiable according to a reward-modulated Hebbian rule According to the theory of the neuronal group slection, selective each network response (R=+1, correct, R = -1, incorrect), pre is the reciprocal connections in a proces called „reentry“ [17]. Reentrant , where R is reward signal provided after dynamical links are formed between distant neuronal groups via presynaptic unit and post the postsynaptic unit [2]. The reward signalling establishes correlations between cortical maps, within than feedback in both connectivity and function in that is not simply signal R influences the stability of workspace activity through or between different levels of the nervous system. It is more general ∆=−wwpost,, pre 0.2(1post pre ) , where w´ is a short-term multiplier on used for error correction or cybernetic control in the classical a short-term depression or potentiation of synaptic weights:

sense. [18] The reentrant interactions among multiple neuronal Naïvethe excitatory Bayes synapticRating Predictionweight from unit pre to unit post [13]. maps can give rise to new properties. [19,20]. The origin of slow neural network models are developed to stimulate some features of brain waves due to „pulling together of frequencies“ [21]. Many . Each node connected to Use of the naive Bayes classifier can be compactly represented all others nodes, the class label , and the components of the input vector wij C XX1... M human brain like recognition and learning as a Bayesian network with random variables corresponding to j on neuron i denotes the strength of the influence of neuron other nodes, i .[22]. The total input on are I i X j , the sum of an external input and the inputs from the . The Bayesian network reveals the primary modelling assumption node . C ∑ is external input on node present in the naive Bayes classifier: the input attributes = = + PC()= c , the prior probability that the i hi wx ij j I i independent given value of the class label . A naïve Bayes classifier j class label C takes value c , and P() Xj = xC = c , the probability that requires learning values for

Cite this article: Robert Skopec. DOI: . 5455 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

The Intelligence as Convergence Cc= ()ij is self-adjoint, i. e., ccij= ji for all i and j , then the matrix that is, the neurons oscillate with equal frequencies and constant, wij = 0, neurons In the Hopfield model as Auto-associative Memory insight the network always converges to an oscillatory phase-locked pattern, dont´t feel themselves, if ww= ,there are symetric interactions computation means covergenceij to ji fixed poits. If (unlikeli to hold in real brains), or only one node is updated at a time but not necesarily identical phases [29-31].There could be many (asynchronous dynamics). The system has a Lyapunov function, such phase-locked patterns corresponding to many memorized function nn images. The proof follows from the241 existenece of an orbital energy E() z=−− ( r z z − c zz ∑∑i i i ij i j . which he referred to as the „energy function“ [23]. This is a useful i=1 2 ij,1− trick for analysing dynamical systems, as it can be used to prove convergence to a fixed points. If there exist a Lyapunov function, the The Hoppensteadt and Izhikevich self-adjoint synaptic matrix dynamics always converges to a fixed point. Hopfield thought of this rules [30]. If all oscillators have equal frequencies, i. e. ωω=... = arises naturally when one considers complex Hebbian1 learningn as a physics problem: he called the Lyapunov function the „energy“, , and the connections function H ij have pairwaise odd form, i. e., wwij= ji , is like the law and thought of the dynamics of the network as a physical system HH(−=−ψψ ) () for all and j ,then the canonical phase model „for every action is an equal and opposite reaction“ [16]. Lyapunov ij ij i evolving to the state of lowest energy. So φii()tt→+ ωφ1 for all i , so the

neurons oscillate with equal frequencies (ω1 ), and constant phase 1 ΣΣ ∑ ∑ Θ converges to a phase locked.pattern functionL=− is satysfying wij xx i j − the xI i idefinition +Θ x i i , where above: i is a threshold for node relations (φ ()tt−=− φ () ϕϕ. In this sense the network dynamic is 2 ij i i i j ij i patterns σ H= −Σ di jiσσ j.Where i is the state of . Hopfield proposed a neuron dynamics which minimize the synchronized. There could be many stable synchronized the ith neuron of the network and dij based on the observation that the phase canonical model has the following energy function between neurons i and j corresponding to many memorized images. [32-35]. The proof is is the synaptic strength 1 n . (Belifante, 1939) =φφ − where is the antiderrivative . energy functionER()φ ∑ . ij() j i Rij H ij 2 ij,1= The Hopfield model of associative memory has provided fundamental insights into the origin of neural computations. The physical signifikance of Hopfield´s work lies in his proposal Neural synchrony is a candidate for large-scale integration[36]. of energy function and his idea that memories are dynamically Distant functional areas mediated by neuronal groups oscillating nonlinear physics into neuro- and information sciences. Closed stable attractors, bringing concepts and tools from statistical and in specific frequency bands enter into precise phase-locking during cognitive and emotional activity [21]. Phase-locking synchrony is loops existing between the talamus and cortex are involved in the the relevant biological mechanism of brain integration [37]. The create oscillations in the membrane potential, which are at the stability of the coupling is estimated by quantifying the phase- genesis of the alpha waves. Two sorts of „slow“ ion channels could ( f t ): locking value (PLV) across the cycles of oscillation at the frequency 1 1+δ /2 ) at any given time =( ϕ τϕ− τ τ begining of the internal genesis of mental objects and their linking PLV(,) f t ∫ exp((jyx (,) f (,))) f d δ 1−δ /2 together [2]. The convergence of two oscillations of given neuron Perception results, if they were in phase, is an amplification [24] of the latent oscillations [25]. Groups of neurons in the brainstem extending a global influence due the divergent nature of their axons. Nuclei in Nonlinear systems are able to benefit from real chaos by using the reticular formation regulate the transfer of sensory messages thermal noise in a highly constructive way, which may even increase and participate in global forms of influence. A particular nucleus order. Perception is a new emergent property, inherently connected containing dopamine – the A 10 nucleus – plays an important role with self-organization. The same class of dynamical processes, in this distribution of the converging influence [3]. which allow the organism to maintain a dynamical equilibrium, also allow the system to perceive its environment. Any self-excited This recognising actvitity represent the intelligence generator because it is able to perceive dynamical processes in an object- oscillator, for example our ear, performs some kind of reification, regulating the selective contact of the brain with the outside world in converging manner. The reticular activating system provide like representation. To come to life, the self-excited systems must a background of excitatory impulses into the cerebral cortex, with be driven to thermal non equilibrium[38-39]. From a dynamical an array of probabilistic quantal emissions that are targets for systems view, the temporal self-structuring ability of self-sustaind the quantum probabilistic fields of mental influence. The firing of oscillators is based on an interplay between the generator and neurones is modified by alterations of the probabilities of quantal resonator, two components governed by two different types of emission of the synapses engaged in actively exciting them [26,27]. active element. The resonator can be modelled as a linear dynamical processes. The generator is a nonlinear, energetically Changes in activity of a module means the changing probability of emission in thousands of active synapses. The Lyapunov energy freedom. and due this process is information probabilisticaly distributed in energetically passive system ot two mutually connected degrees of function is organizing the convergence of the energy to information, theMemory brain as intelligence [28]. Through its negative resistance the generator process transfers energy from a steady energy source to the various oscillating modes of the resonator, which limit the energy flow through the the all neurons have equal frequencies ωω1 = ... n ,and the connection In the synchronization theory for oscillatory neural networks if system, allowing the oscillations to settle in a steady state, when the

Cite this article: Robert Skopec. DOI: . 5456 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

(()())L EEG= c EEG . The pyramidal neurons contain f energy supply equals the losses [40]. The linear passive resonator order HEs sizes The part s process is governed by the superposition principle. The new effects magnetic crystals1 and haemoglobin molecules, which are magnetic. f e ) and the and part s as screen sensory input i, projected and monitored by occur due to the processes in generator [41]. There are nonlinear 2 f ). They depend on the frequency can be interpreted as possibly moving screen of GW, interactions between the externali signal (frequency difference ∆=f() ffei − internal signal (frequency an external observer[23-24]. The work of the torsion generator is , the amplitude of the driving signal, and the based as well on spin systems of physical vacuum and manifesting degree of coupling [42]. In adition to phase locking and frequency itself by torsion fields. This provides for the possibility of direct pulling, further nonlinear effects can be noted. On both sides of the access of the operator to the processor without a translating locking range new frequency components will appear. A damped The operator can, on basis of arbitrarily chosen protocol, enter its own internal rhythm in the stady state [43]. Contrary to that, periphery [49]. harmonic oscillator follows passively a driving force, forgetting nonlinear autonomous oscillators in the phase-locked state actively into such generator without any mediating apparatus, by way of direct interaction directly with the central processor through the follow the driving signal. The system has perceived the driving molecular level is encoded not only in the structure of molecules, channel of torsion exchange of information. The information on signal, when switching from the unlocked to phase-locked state.internal but as well in the structure of the medium which surrounds dynamical processes prefectly reconstruct the periodicity of the it (environment) and this information is connected with spin The system changes the mode of behaviour, and the

can propose that the brain as a functional structure of internal external signal. In the case of weak coupling its perceptions and polarization of physical vacuum, it means with torsion fields. We about new combination products. Perception in the most basic conscious representations includes a bio-computer, it means a actions reduce to a modulation of the internal rhythm, bringing representations. Perception is more than a passive process, psychological sense means active construction of internal Hopfield system, and its outside part - the torsion generator of the perceptions and actions are interlocked [44]. They are closed to space around the brain [50-51]. cerebellum interacting by the spin polarized physical vacuum in the Atoms of Space and Time in the Brain a perception-action loop. The generic nature of self-organization mental world. phenomena, even bridging the gap between the material and be rewritten, in terms of the information as it evolves on the screen. Environment´s Broadcast to Brain´s Neg(Entropy) The holographic principle says that the laws of physics should Transform Here information flows from event to event, and is Where “interactions with the environment” under everyday defined by the measure of the information capacity of a channel by which information is flowing [52]. It would be the area of a surface, some derived quantity. The area of some surface would turn out experience masks the quantum, there lies the heart of darkness so that somehow geometry that is space, would turn out to be – and there is a center of the glittering prize. John A. Wheeler There are another two principles mediating the interaction with to be an approximate measure of the capacity of some channel in a transition to a computational metaphor in physics. For the screen environment. The first by Smolin is background independence. the world would fundamentally be information flow [53]. This is as a surface is possible to use Mandelbrot´s fractals which behave This principle says that the geometry of spacetime is not fixed. as the To find the geometry, we have to solve certain equations that is diffeomorphism invariance. This principle implies that, unlike include all effects of matter and energy [45].The second principle dimensions of the structure greater than spatial dimension by the formula: [54].Hausdorff-Bezikovich dimensionlog(NE of (δ ,surface )) boundary is given dimB (E )= lim − theories prior to general relativity, we are free to choose any set of log(δ ) coordinates to map spacetime and express the equations. A point δ → 0 δ ≥ 0 in spacetime is defined only by what physically happens at it, N Diffeomorphism invariance is very powerful and is of fundamental dN where proved that: N(δδ , E )= inf N ∈∃ N / ( x1 ... xni ) ∈ ( R ) / E ⊂ UB ( x , ) . not by ots location according to some special set of coordinates. { i=1 } log(AE (δ , )) dim= lim − where A(δ , E) . B δ importance in [46]. Biological Effects of Torsion Fields are realized log( ) physical vacuum makes it possible to formulate principally new The another mode of computation of the screen as a surface is due the torsion fields as meta-stable states of spin polarized also possible [55] approach to the quantum (torsion) generators. The Unexpectedness Maximization Algorithm structures associated with the primary and secondary sensory The Vacuum domains are acting trough a magnetic flux tube

The learning algorithm in biological systems is partly organs which define define a reentry in hierarchy of sensory based on the negentropy maximization, different from the representations in the brain [47]. The GW is the place of coding by associates them with various sensory qualia and features, for machine learning [56]. Shannon is also thinking in terms of magnetic transition frequencies which generates sensory subselves, information as a measure of unexpetedness (innovation) in his proposed information H „A Mathematical Theory n Of Communication“ (1948). There was example by quantum entaglement. Heaviside Electrodynamics pi ). our level in hierarchy of projections [48].The sensory selves are of as the propbability of unexpected, rare, (HEs) define this sensory projections and EEG HEs correspond to unforseened, as the probability of unexpected (

Cite this article: Robert Skopec. DOI: . 5457 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

Hn= −Σ pP ii.log The Resonance

to be of a crucial importance. [61,62].The requirement that the For a given question (Q constant) is possible to have different For our representations the resonant amplification is expected states of knowledge. Shannon defined the information in a message of probabilities, and to a new value of S (entropy). in the following way: A message produces a new X, which leads to Maxwell Electrodynamics (ME) acts like a resonant wave cavity fixes To obtain a measure of the information He proposed that the f= cL/ L a new assignment the representations to a high degree. The fundamental frequency of ME of length equals to the magnetic transition f= cL/ = f. information (I) be defined I by= SQX the( difference )( − QX1 )the two uncertainties: frequency at the sensory organs: m

Information is always causing change in probabilities, which is This implies that various parts of the magnetosphere probably it also. Shannon´s measure of information as an invention, which the condition L= kS , where the S is the transversal thickness of the changing the energy levels, and therefore by transitivity a mass do correspond to various EEG bands. For the personal magnetic canvas due changing the amount of uncertainties is making external work. magnetic flux tube paralell to ME, guaranties resonance condition information). The probability amplitudes of quantum mechanical (complementarity of topology and probabilities, e. i geometry and for all points of ME. Continual transformation of magnetic energy to information (magnetometabolism), might be a key aspect of probability amplitudes, [63] The cavity resonances associated with the spacetime sheet processes must be complex. The Hilbert space need a complex correlations between consciousness and the magnetosphere. when distinguishability between the two the two state vectors [57]. Any quantum evolution ot he two-state populations is measurred by the angle in Hilbert space between complex defined by Earth allow transversal communications and amplifications. Besides 7.8 Hz Schumann resonance associated the pure states into the ellipsoid containt in the Bloch sphere. Earth´s core of the Earth deserve to be mentioned since they are system can be thought also as a transformation of the surface of with Earth, also the 40 Hz (14 Hz) resonance associated with rt() resonaces are important too [13]. [58] The complex number can be expressed as the sum of important EEG resonaces. Of course, also the harmonics of these differences ∆ω the complex phasej factors rotating with the frequencies given by Influence Functional between the energy eigenvalues of the interaction Hamiltonian, weighted with2n the probabilities of finding these = −∆ω energy eingenstates inrt( the ) ∑initial pjj exp( state: i t ) . The Conscienece: a cryptic code of “Back Doors” influence j=1 functional in the cortex. It is also proven experimentally that j the microlepton fields can be excited by electromagnetic field. environment of the interaction Hamiltonian (tensor products of The index denotes partial energy eingenstates of the The presence of a magnetic moment in microleptons causes ↑ and ↓ polarization of the magnetic dipoles of the microleptons and that The Wignerstates Distribution of the environmental as Probability spins) [59]. polarization causes the creation of standing waves (periodic space structures) in microlepton fields [64]. The microlepton fields can carry information and modulate magnetic moment. In general, Contrary to the well-established models, we are proposing there are evidences that microleptons can carry energy, impulses that the Wigner distribution can be regarded as a probability and information (Ochatrin, 1996). Our earth is part of a „sun-earth- because W (x, p) A system from equilibrium, the sun becomes a source of information and distribution for the unexpected, but really becoming events, x space“ system, that is out of equilibrium. By providing a departure is real, and can be also negative [60]. and p described by this type of Wigner distribution is localized in both useful energy. Man´s use of energy on the earth´s surface constitutes , is leading to the expanded complex probabilities causing (especially in brain-mind) processes: internal transactions with energy fluxes that are thermodynamically the high impact of pseudo-„unreasonable“ negentropy in biological 2 22 usable [65].This negetropy flux derives from the energy flux from 1 (xx− )( pp −∂ ) systems, the earth creates subsystems out of equilibrium with the Wxp( , )=−− exp 11 22 the sun to the earth. By storing the energy and negetropy in various π ∂ Entanglement the earth´s surface are due to solar processes. general environment. Both the energy flux and the negetropy flux at If the system is more isolated, than it interacts with the used to affect physical processes is of the order of 1038 bits per The maximum steady-state rate at which information can be apparatus only briefly. As a result of that interaction, the state of ()∑iias i A0 →∑ii as i A i. appartus becomes entangled with the state of the system: second. The possible amplification of information-processing as This quantum correlation suffers from the basis ambiquity: The activities is much greater. [66]. The Influence Functional is defined F[ x, x] = dqdq dq pε( q q ) D D exp( i ( Sεε[ x , q]− S[ q , x ] )) 1 ∫∫0 1 0, 1 qq1 S S 1 1

where pε is the initial state of the environment and SSε [ qx, ] is S – A entanglement implies that for any state of either of the two systems there exists a corresponding pure state of the other. The system). If there is no interaction (or if two systems trajectories are roles of the control system and of the target system (apparatus) can the action of the environment (including interaction term with the the same, i. e. xx= 1) be reversed when the conjugate basis is selected. (Gazzaniga,1995) Fx[]= x1 These ambiquities that exist for the SA pair can be removed by , then the influence functional is equal to one. recognizing the role of environment [28]. is the so-called influence functional [67]. This functional is Cite this article: Robert Skopec. DOI: . 5458 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

These phases in a macroscopic apparatus coupled to the responsible for carrying all the effects produced by the environment on the evolution of the system. If there is no coupling between the environment are fluctuating rapidly and un controllably, thus this consequence of decoherence were somehow prevented (by system and environment, the Influence Functional is equal to the leading to the destruction of phase coherence. However, even if by a set of independent oscillators coupled linearly to the system is Identity. Calculating the Influence Functional for an environment perfectly isolating the apparatus pointer from the environment), selected by the environment [68]. would have to be known while, simultaneously, A is in A0 , the a rather straightforward task [48]. The energy eigenstates are also preexisting phases between the outcome states of the apparatus „ready-to-measure-state“ [48]. Π ε is a momentum operator (as the environment operator acts on the The decoherence in eigen basis can be established Physical Properties of Vacuum Domains environment is a coherent vacuum state, the evolution turns out There could be relevant to propose a possible new information environment as a translation generator), and the initial state of the to be such εεn ()t=0 + Stnn , where SSnn= ϕϕ n n . Therefore the channel in Universe, where the greater part of the potential energy of 2 22 εεn(t ) m ( t )≈− exp( tS (nn − S mm ) ) . Consequently in overlap between the two states that correlate with different energy interactions is transformed to the spin[55]. The energy of gravispin eigenstates can be waves, reaching the vacuum domain - VD from the absolute this case, there is einselection of energy eigenstates (superpositions physical vacuum – APV, is transformed inside the VD into energy of energy eigenstates are degraded while pure energy eigenstates of electromagnetic waves. The energy of electromagnetic waves are not affected). For this reason, pointer states are energy reaching the VD from the APV, on the other hand, is transformed eigenstatesDecoherence [69]. Functional inside the VD into energy of gravispin waves. In a gravitational field, a VD becomes both an electrical and a gravitational dipole; i.e., the VD in this case creates both electrical and gravitational fields inside The decoherence functional is defined in terms of which the and outside itself [71]. In a magnetic field, the VD becomes both a decoherence process determines the relative fitness“ of all possible magnetic and a spin dipole; i.e., it creates both magnetic and spin superpositions that exist in Hilbert space [47]. The resulting natural fields inside and outside itself. In an electrical field, the VD becomes selection“ is responsible for the emergence of classical reality[56]. both an electrical and a gravitational dipole; i.e., it creates both superselection, or einselection. In a quantum chaotic system In the GW its consequence is known as environment-induced electrical and gravitational fields. In a spin field, the VD becomes weakly coupled to the environment, the process of decoherence both a magnetic and a spin dipole; i.e., it creates both magnetic and spin fields. The VD functions simultaneously as a converter of will competeλ with the tendency for coherent delocalization, which energy and a transformer of two types of waves and four fields [16]. occurs on the characteristic timescale given by the Lyapunov The VD introduces four polarizations and, consequently, four exponent . Exponential instability would spread the wavepacket additional fields into a solid. Four tensors of striction stress are to the paradoxical size, but monitoring by the environment will associated with the fields. The tensors of striction stress related attempt to limit its coherent extent by smoothing out interference comparable: to the gravitational and spin fields can be incorporated by analogy fringes. The two process shall status quo when their rates are with the tensors related to the electrical and magnetic fields[72]. In Τ≈D ()δλx 1 addition, another tensor is related directly to the spin polarization: [73]. All these stresses alter the initial stressed state of the solid, Because the decoherence rate depends on δ x , this equation a nonsymmetrical tensor of tangential spin mechanical stresses

yields lTc≈Λ dB () × λγ / . which is characterized by the tensor of initial mechanical stress can be solved for the critical, steady state coherence length, which The striction, spin and original mechanical stresses, normal and macroscopic will be forced to behave with as Circulationtangential, are summed of Energy up algebraically[74]. and Information due the All of the systems that are in principle quantum but sufficiently a result of the environment-induced superselection. This will be Development of Gravispin Waves in the Universe << lxc , because lc is a measure of the resolution of „measurements“ carried out by the environment [70]. The basis-dependent direction radiation, especially the self-luminescence, it is necessary to As it was stated above that for explaining electromagnetic is a direct consequence of complementarily. It can be summed up of the information flow in a quantum c-not (or in pre measurement) assume that space is filled with gravispin waves with a high power flow density in any preselected direction. In this connection, the { 0 ,1} travels from the measured system to the waves? In the study this question is considered in part by the by stating that although the information about the observable with apparatus, in the complementary { +−} basis it seems to be the question arises: what and where are the sources of gravispin the Eigen states use of closed chains of transformations of energy, which are apparatus that is being measured by the system. This observation in measurements. They are not really destroyed, but, rather, as the included naturally in models of combined electro gravidynamics also clarifies the sense in which phases are inevitably disturbed: apparatus measures a certain observable of the system, the system the non-homogeneous PV, specifically the mutually reversible „measures“ the phases between the possible outcome states of the transformations: electromagnetic energy to thermal (EM ⇔ T); apparatus. electromagnetic energy to mechanical (EM ⇔ M); electromagnetic energy to gravispin (EM ⇔ GS); thermal energy to gravispin (T ⇔ GS); gravispin energy to mechanical (GS ⇔ M); mechanical energy Cite this article: Robert Skopec. DOI: . 5459 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

The Transformation of Energy to Information due the Gravispin Waves to thermal (M ⇔ T). GS energy must be understood as three types of energy: gravitational field, spin field and the energy of gravispin waves. The EM energy also must be understood as three types waves[76]. In electro gravimechanics, any system, including human of energy: electrical field, magnetic field and electromagnetic brain, is a receiving gravispin antenna that absorbs the power of outside gravispin waves. This power increases the kinetic energy T; normal mechanical systems, the loss of power related to friction The model of a non-homogeneous PV brings up problems of the of movement in mechanical systems, but only very slightly. In study of little-known transformations of energy: EM ⇔ GS; GS ⇔ and two particular transformations: GS ⇔ M, of spin energy to mechanical and the reverse covers the inflow of the power in question practically completely. mechanical and the reverse, as well as of gravispin wave energy to The transformation of gravispin energy into mechanical energy [75]. We examine transformations of can play a significant role [83].Problems of the transformation problems of Heaviside. The formulae for the absorption of the energya) indicated: of gravispin energy into mechanical energy are similar to the The transformation of the energy of gravispin waves to b) mechanical energy; energy of gravispin waves can be obtained from formulae of the emission of the energy of electromagnetic waves by substituting for ( for ), and –c c. c) The transformation of heat to the energy of gravispin waves; masses with a minus0 0signG 0 for electrical0G charges with a plus sign, The reversible transformation of the energy of electromagnetic the coefficient ε ε μ μ for the speed of light waves to the energy of gravispin waves. As an example, one can obtain the formula for the transformation ρ , J in the equations of gravispin energy into kinetic energy (power). We must use the The three latter transformations of energyG G are related to respect to the surface of a sphere of radius R Umov-Pointing theorem, integrating the Umov-Pointing vector with the reversed signs in front of the “sources” → 0 seeing it as a point of Heaviside. First of the transformations of energy indicated movement is not an emitter (radiator) but an absorber (absorbent) with a given mass. that a gravity antenna in the form of a point body in accelerated We can use the expressions for electrical and magnetic fields of to perform the substitutions indicated above. With such an of the energy of gravispin waves [77]. Interesting is situation with a point electron in accelerating movement, in which it is necessary a transformation of the energy of gravispin waves into mechanical r R energy. approach, the gravitational field in a nonrelativistic approximation mr m r[ rv ] is expressed by the relationship=−− (with ≤ → 0): Instead of the expected source of gravispin waves, we obtained EG 3 23 4πε0G r 4 πε 0G cr an absorber of the waves. Outside sources of gravispin waves condition that the principle of causation not be contradicted [24]. are required again for describing an absorber of energy on the m[ vr] mrrrv[ [  ] ] 2 H =−+ σσG −> σ1 0 The spin field is expressedG 34as follows: The second transformation of energy – a transformation of heat to 4ππ r 4 cr the energy of gravispin waves that on the condition (r m due to the heat in the substance [78]. Third is transformation of , the gravispin waves passing through a substance are strengthened is the mass; v and v are vectors of the velocity and acceleration). is a vector-radius, the origin of which is located at a point; electromagnetic waves into the energy of gravispin waves and EM, EM T In an approximation of circular orbits, where the acceleration vice versa – do sources of gravispin waves appear [79]. Despite power follows from: 2 and M is perpendicular to the velocity, mv the2 (  following) expression for the the partial reversibility of the transformations M ⇔ ⇔ −ΠdS = =W, ∫ G πε 3 ⇔ T, the prevalent total energy flows are in the direction S 6c0G of thermal energy i.e., M→EM→T, M→T. A continuous increase where dS is an oriented element of the surface of a sphere in thermal energy occurs at the expense of mechanical and of radius R G = [EGHG 2 electromagnetic energy. This system includes the transformations (υ = υ / RC ) used by the → 0, and Π ] is an Umov-Pointing vector. The of chemical and nuclear energy. Part of these types of energy is plant follows: following formula for the gravispinm24 υpower transformed into heat, while the mechanical and electromagnetic = W,2 energy emerging due to the transformation of chemical and nuclear 6Rπε 0G C (R energy in the final analysis are also transformed into heat [80-82]. C The prevalent energy flows form four energy cycles: The Transformationis the average distance). of Heat into Energy of Gravispin T→GS→M→EM→T and T→GS→M→T, in which the VD-NSLF do Waves in the Absolute Physical Vacuum not participate directly, and T→GS→EM→T and M→EM→GS→M. The energy transformations T→GS and GS→M are weak. This the case of the APV, can be considered at 1 = 0 and 1 = 0, since The removal2 2 of heat2 from2 a substance by gravispin2 waves, in means that at the places in the Universe where intense energy ε ε >> ε = ε ; µ µ >> µ = µ , σσ ≅ σ reverse transformations associated with a decrease in entropy can 0 0G 11 1 0 0G 11 1 but with G 1 . In the case of processes take place with a significant growth in entropy, the ε μ however, are widespread in the Universe, due, in particular, to the remain unnoticed. The reverse energy transformations T→GS→M, monochromatic plane linearly polarized waves, electromagnetic and gravispin fields can be represented by the following relationships: exceptionally high penetrability of GS waves into matter. Cite this article: Robert Skopec. DOI: . 5460 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

E= Im E exp iω ( t+ sx) ; zz the distance λ energy of one type of waves∆= intox the other takes place inside VD at aa+  εµ Hyy= Im H exp iω ( t+ sx) ; -1/2 -1/2 e=e1 /(e0e ) ; am=m1 /(m0m ) (ae, am£ 1). (l is wave length; a 0G 0G where s is a parameter with an inverse velocity dimension; iωs i is The domains magnetic field is connected with the polarization ω is the angular frequency;   Ez , EGz , Hy , HGy = γ is the wave propagation constant; of VD that arrise as a result of the effect of the Earth fields. For the y and z coordinates; x a) the imaginary unit; are complex amplitudes of fields spherical domain these polarizations are expressed as follows: aa2 PE= εεεε− on the axes of is the coordinate along which ε 220G 00 electricalη0 (1−−aa εε polarization / 9) 3(1 of /the 9) domain the waves propagate. The reversible transformation of the energy body of a vacuum domain: b) of electromagnetic waves into energy of gravispin waves inside the aaη 2 = εε0 ε − c) gravitationalPG 22 polarization00E of eEthe0GG 0domain (1−−aaεε / 9) 3(1 / 9) According to the total power flow of electromagnetic and section perpendicular to the x yz), i.e., d) 2 gravispin waves averaged over time has the same value in any aµ a P= HH− ε e) magneticMS polarization2200 of the domain axis (in the planeE0 H 0 µ0 (1−−aaεε / 9) 3(1 / 9) Ð + ÐG = Ez H y + EGz H Gy = . 2 f) aaη 2 PH−−µµ0 H spinSS polarization2200 of the domains (1−−aaεε / 9) 3(1 / 9)

The expressions for fields represent a uniform solution for both (E0 2 13 electromagnetic and gravispin waves. This solution demonstrates s ); H 0G = 0 is electric field (130 V/m); E 0S gravitational field (9,83 m/ that the energy of an electromagnetic wave passes into the energy the Earth). VD penetrates into the matter, it acquires the electric is magnetic field (19,5 A/m); H = spin field (10 kg/m×) of of a gravispin wave, and vice versa. (Benaceraff, 1964) The period aε qV= − ρG . for full transformation of the energy of an electromagnetic wave η relationship monocharge 0 2π λ into the energy of a gravispin= wave= and back is expressed by the Äx −1 −1 -1 −1 , (υ + −υ − )ω c(υ + −υ − ) When VD penetrates into the matter with this monocharge it is the density of the causes the electric current of the free electric charges that aim at where λ is the electromagnetic waveM length. The PV is the neutralization of the monocharge.22 This current is responsible (ρ ).RV . characterized by four polarizations: P = WT 2 of PV; P is the density of theG spin moments (spins) of PV or the for the liberation of the heat energy.5εε (1− a / ε ) magneticS moments of PV; P is density of the gravitational dipoles 0 ε -1 (r’=-ae 0 r ; R is domain radius). PE and PM are related as well as P and PS density of the angular momentum of PV. According to polarizations ×h G P and PG P are completely independent of The Skopec‘S Replacement Function E M S polarizations, but the two each other [46,84]. pairs of polarizations P G Skopec’s Replacement function: On the possible Conversion of Mass, Energy and Information: Sk= log W Since PV quarks have electrical charges and mass, magnetic and spin moments, electrical and gravitational polarizations can S= klog( cI / ) be related in PV as well as magnet and spin polarizations. This −Sk/ Heaviside’s vacuum equations. I= ce connection between PV polarizations can be introduced to the −Sk/ A solution of this problem: the connection between electric and I= () I0 ce I= () I ce−Sk/ only in the local areas. So in PV theory an object has appeared that 0 gravitational, magnetic and spin polarization was assumed to exist E= mc2 was called VD[85] .The mathematical model of the vacuum domains 0 (1−ν 22 /c ) based on the discussed notions can be presented as follows: E= mv0 ∂B ∂D (1−ν 22 /c ) div D = ρ , rot E = − , div B = 0, rot HJ= + , etc., ∂t ∂t = ν ph/ c where E, E vc= / λ G are electric and gravitational fields in the moving the conservation of momentum and the law of the conservation of ph= / λ bases. Inside the domains the energy conservation law, the law of the impulse are observed. Self-luminescence of vacuum domains λ = hp/ takes place because gravispin waves energy is transformed into electromagnetic waves energy. The complete conversion of the Cite this article: Robert Skopec. DOI: . 5461 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

λ=hm/ νν / (1 −=−22 / c ) h (1 ν2 / c 2 ) / m ν [ 00 . quanta’s physically are, and technically will be possible [86]. At the speed different from c there is transformation of It means that conversion of Energy quanta’s into Information The Torsion Transition energy quantum to the information qunta,(where I = information, ϕ pxx(,1 ) W= work, Q = energy, S = entropy, p = probability, ln, or e = the The torsion effects of VD could act through the termal Skopec’s Replacement Function excitations of the field . In case of the density matrix natural logarithm, etc.). master equation: of the particle in the position we can write down according to the SQ( / X )= K∑ p11 ln p /Shannon/, d i ∂∂ 2mk T . p=−(,)()()Hp −−γ xx pp − −B ()xx −2 p S = k.logW d 11∂∂ 2 t xx1

k.log W= − Kpp∑ 11ln /Boltzmann/,, γ ( H = a particle´s Hamiltonian, k B = the I = S(Q / X) − S(Q / X ) 1 , T =relaxation rate, ) operation I= − Kpp∑ 11ln Tn , Boltzmann constatnt,f = the temperature of the field) [87-88]. We in the manifol Cab S = k.logW have an n-parametric simple transitive groupj (group , ξb

are structural constantsi 1 of i the group that obey I=−= K pln pk .log W Ω=jk C jk ,the components of ∑ 11 , the Jacoby identity. The vector field is2 said to be infinitesimal 1 2 W= mv generators of the group. We see that 2 , of absolute parallelism are constant. It is easily seen that the the anholonomity object (Ricci´s torsion) ofk a homogenous ik1 spacei 1 2 connection possesses torsion. In this case ∆ij = −Ω jk =TC ij = − jk Ek = mv , 2 2 1 i where − C jk is the torsion of absolute parallelism space. It 1 2 , 2 −=K∑ p11ln p k .log mv 2 connection with torsion 1 2 was exactly in this manner that Cartan and Schouten introduced I= k.log mv WE,= ∗ 2 , = k , , Γ=Γ+ijk ijk()SSS ijk − jki − kij = I = W,

I = Ek , where Sijk is the Cartan´s torsion of the Riemann-Cartan SQ( / X )= − K p ln p space. The Ricci´s and Cartan´s torsions have the same symmetry ∑ 11, dQv SSS−=x1 1 x , T properties, but Ricci´s torsion depends on angular coordinates S = S − S 1 , unlike Cartan´s one. Moreover, Ricci´s torsion defines the rotational SQ( / X )= − K p ln p Anti-EntropicKilling-Cartan metric, Intelligence but Cartan´s Generatornot at all [88,89]. ∑ 11,

S = S1 − S = S1 − S2 represented as a Bayesian network with random variables x1 dQv SS= x Marlin use the naive Bayes classifier can be compactly 2 T , C ,and the components of the

input vector XX1... M .The Bayesian network reveals the primary dQv corresponding to the class label −==K pln p SSx1 ∑ 11 x2 T , input attributes X j x dQv modelling assumption present in the naive Bayes classifier: the =ISS = = 1 x2 C PC()= c T , are independent given value of the class label , the prior probability that the class label C takes value c , and . A naïve Bayes classifier requires learning values for There is a transformation between thermal and mechanical = = P() Xj xC c ,the probability that input feature X j takes value x C = c [55]. As perception feature I = E energy: , given the value of the class label is E = m.c 2 mutual information between the class variable and each attribute , selection filter is the naïve Bayesian classifier based on the empirical 1 E= mv2 k 2 , variable. The mutual information is computedP( Xj = xC, during = c) the learning MI( xjj , c )=∑∑ P ( x = x , C = c ) log E = E xc PX= xPC( = c) . k , process learning [69]. ( j ) = a a E W To predict the value of ry r of a particular active user a Due to our replacement function we have proved that the Atoms given the profile of Space and Time are the basic quanta’s of the Information too. we apply a slightly modified prediction rule to allow V ∂ a for missing values.a The prediction rule is (rwj ,). ry= arg max PR( z= v) PR( jy= wR = v) E = h.v , v   j≠= yw1

Cite this article: Robert Skopec. DOI: . 5462 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

p(µ /) y= kp ()(/ µµ p y ),

I tis well-established that the population dynamics is using where py(/)µ an ensemble approach. The population thinking is the essence of in phase space. The cloud is described by a function p(q, p,t) , the ensemble µ a result of the probability filter effect, k is the is distribution function of the living state in Darwinism [90]. An ensemble is representedt by a cloud of points p(µ) is the a priori of phase space around the point q, p. A trajectory corresponds function of the ensemble µ , and p(y/ µ) is the distribution which is the probability of finding at a time , a point in the region constant from the conditions of normalizing, p q0, p0 . Function that µ y vanishing everywhere except at the point function diclosing stetes of the ensemble , the probability filter so called Dirac delta functions. The function ∂(x − x 0 selects as living ones. have the property of vanishing everywhere exceptp a single point, are for all points x ≠ x0 . The distribution function sum up the probabilities of alterntives microsttes, but multiply ) is vanishing By calculating statistical wights of the states, we must not . get the form: p=∂−∂−( qq00 )( pp The Hamilton Convergence conditional probabilities of all succession microstates composing the tickness of the given macrostate. The multiplication led to the statistical values les then one, and to negative value of entropy. N. evolution equation, The Ricci flow equationd used by Richard Hamilton is the Kobozev proposed to name this process as „anti-entropy“ [91].The gt()= − 2 R ij ij re-normalization is a constant outer interference, from macrostate dt , element of „miracle“, is the discrepancy between observed having his image, into the natural dynamics of microtates. The for a riemannian metric gtij (). It is proved that Ricci process at a microlevel for the second law of thermodynamic an preserves the positivity of the Ricci tensor in dimension three uncertainity of the future is introduced into the natural dynamics. flow The measure of this „miracle“ element is anti-entropy, and the of the Ricci tensor three and curvature operator in dimension four and of curvature operator in all dimensions, and the eigenvalues renormalization o the probailiies is the mechanism of anti-entropy are getting pinched pointwisely as the curvature is larging. This is origination.Appearence of the living statesis only in biological proving the convergence results, the curvature converge modulo systems probable. The biosystems are aranging a re-normalization scaling to metrics of constant positive curvature. Hamilton proved of probabilities of the states determined by higher structural level. a converegence theorem in which such scallings smootly converges The re-normalizatio of probabilities is characteristic for all levels modulodiffeomorphisms to a complete solution to the Ricci flow. of the biological hierarchy. The Bayes equation is the mathematical The Ricci flow in quntum field theory is a approximation to the description of re-normalization of probabilies. renormalization group (RG) for the two-dimensional nonlinear A Bayes approach is a theory reflecting that from the certain level model. The connection between the Ricci flow and the RG flow of life the re-normalization of probabilities is realizing the function levls from a cell up to the biosphere is reduced to a multiplicative show that the flow must be gradient-like [87]. The lens spaces with of the probability filter. This re-normalization of probabilities at ll finite cyclic fundamental group constitute a subfamily classified microlevels. The probability amplitudes: up to piecewise-linear homeomorphism.The Ricci flow equation interaction of the functions o states for corresponding macro- and 2 22 2 2 led to the sphere collapses to a point in finite time. The theorem ψ =++c1 u 1 c 2 u 22 ccuu 1212. of Hamilton is saying that, if we rescale the metric so that the Where u u are the wave functions, and ψ = c1u1 + c2u2 . volume remains constant, then it converges towards a manifold of 1 2 constant positive curvature. Grigory Perelman is pointed that one The duble product is the interference term, and the human a 2-sphere in M 3 way in which singularities may arise during the Ricci flow is that may collapse to a point in finite time. By using his – Lyapunov function and the interference term.The brain is process [86]. itelligence is generated by convergence of the energy function approach „surgery“ on the manifold is to stabilise the convergence Information as Probability Transformator functioning like a mathematical generator of intelligence. We can see the brain as a biocomputer generating consciousness and anti- A complete description of a microstate is the state function entropy. entropy, or the brain as a biogenerator of consciousness and anti- Conclusion (ψ - function) which modulo square is a probability density. I t particles. It is possible to increase the probaility of macrostate only presents itself the result of interaction of ψ - functions of single “You can´t take the problem seriously, unless you have a by re-normalization of probabilities of both microstates. The living solution to propose”. Secretary of State George Marshall. The macrostates of the biological system realizing the corresponding mode can shift to the tail of the distribution. It is a result of Neurobiology is achieving a lot of success in describing the path microstates by means of re-normalization of probabilities, its multiple interactions of the initial, a priori function of probabilities of the stimulus in the organism. But for us is this research a little distribution for all possible microstates, which are in complicated bit one-sided, concentrating only to one way of getting information about the working of the brain. We believe, as it has pointed the which the macrostates performs a determinance [37]. The Bayes Nobelist Sir John Eccles, that brain has a structure of maximal bio-systems not equally probble. The probability filter by means of formula can be used as a mathematical model: complexity, and complexity is possible to study by part after part, but to understand to this complexity, is possible only with multiple of complex approach. Today we preferring this one-sided Cite this article: Robert Skopec. DOI: . 5463 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398 Biomedical Journal of Scientific & Technical Research Volume 6- Issue 4: 2018

convey, each brain is unique, its perceptions are its own creations, empirical methods, but they are not reflecting enough the functions of interactive systems influencing one to another at the complex and its memories a personalized synthesis of these perceptions. The level between organism, brain, and their environment. Knowing individuality is created in ontogenesis by a process of Darwinian perfectly the parts of the system is not enough, because the process selection. The intelligence is an ability acquired by each brain to reductionist approach has had a very successful history with the of interaction is transforming the microstates of the system. This categorize in a manner that facilitates creative associations when information is retrieved. The information coded in DNA are itself discovery of many fundamental principles concerning the parts, not fulfilling all the conditions needed for individual development, which go to make up a whole living organism. Most recently one there are also other factors, other conditions realized by re-entry only needs to look at the achievements of molecular biology. of interactions with environment. There is a lot of influence fundamental aspects of life and brain, which cannot be understood But it is becoming clear, however, that there are some regulated not by DNA, not only by biological mechanisms, but also by physical, mathematical, probabilistic, chemical, gravi-spin, or molecules, cells, and tissues are never isolated, and are not free psychological mechanisms of the re-entry. Also re-entry between from reductionist point of view. In the living state the parts: the same way. Resonance between rational concepts is pleasurable. As to function separately, but rather co-ordinate their activities, the cortex and the limbic system does not always interacting in the Blaise Pascal said, “the heart has its reasons that reason not know”. to generate a unique state of dynamic order which is not found in any non-living system. Thousands of reactions are going on The limbic system and the hypothalamus (together the “heart”) simultaneously in living systems, to constitute the perfectly have enough autonomy vis-á-vis the cortex that, under the pressure at each of number of hierarchical levels: electronic, cellular, organized fluctuations of life. Precies co-ordination is needed of particularly strong sensory stimulation, motivation may increase cortical resonance (“reason”) says “no” to the act in question [2]. to such extent, that the subject “goes into the action” even if the molecular, all the way to the whole organism. However, most of the not suited to study these co-operative mechanisms. And so they due the anti-entropic selection, by the comparison of mental objects experimental methods used in contemporary neuro sciences are The recognizing activity represents a “generator of intelligence”, have until recently been mostly ignored. The alternative requires in terms of their resonance or dissonance. Influence functional to understanding the brain as an interactive system such that will may include also some form of “Higher Intelligence” as a strange respond as integrated system to any stimulus. The Brain is open attractor behind the scene, like the Programmer of the Universe Soft- system, which continually exchange energy and information with ware (“God”). But we are not able to “read in His Mind” because of understood in terms of non-linear dynamics, can respond to a small the environment. Because of its extreme sensitivity, which can be Gödels Theorem of Uncompletness. The Lyapunov energy function is organizing the conversion of the energy into information. The input of energy, but an enormous amount of information. Probability Re-normalization is increasing the external work of the Whatever power we have, the outcome could be influenced new information, causing also topological changes (torsions) in the decides what feature of the electron he will measure, but “nature” only in partial manner. As it pointed J. A. Wheeler, the experimenter system. Changes in the topology makes possible the effect of the Referencesgrawispin powers penetrating into the matter. decides what the magnitude of the measured quantity will be. The 1. complementarily is the deepest-reaching insight that we have ever studied, but its interaction with the environment. How “interaction Baars B (1988) A Cognitive Theory of Consciousness. Cambridge UP won into workings of existence. Not size nor nature of the object 2. with the environment” under everyday situation masks the New York, U.S.A. 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Cite this article: Robert Skopec. DOI: . 5466 Brain as a Anti-Entropic Intelligence GeneratorBiomed J Sci&Tech Res 6(4)- 2018. BJSTR. MS.ID.001398. 10.26717/ BJSTR.2018.06.001398