BioInfoPhysics Models of Neuronal Signal Processes Based on Theories of Electromagnetic Fields (original) (raw)
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Neurodynamics analysis of brain information transmission
Applied Mathematics and Mechanics, 2009
This paper proposes a model of neural networks consisting of populations of perceptive neurons, inter-neurons, and motor neurons according to the theory of stochastic phase resetting dynamics. According to this model, the dynamical characteristics of neural networks are studied in three coupling cases, namely, series and parallel coupling, series coupling, and unilateral coupling. The results show that the indentified structure of neural networks enables the basic characteristics of neural information processing to be described in terms of the actions of both the optional motor and the reflected motor. The excitation of local neural networks is caused by the action of the optional motor. In particular, the excitation of the neural population caused by the action of the optional motor in the motor cortex is larger than that caused by the action of the reflected motor. This phenomenon indicates that there are more neurons participating in the neural information processing and the excited synchronization motion under the action of the optional motor.
2019
The physics of the human brain has two components – basic physics common to all mammals and the physics of thinking inherent only in man. The development of the mental component of the structural and functional organization of the brain in phylogeny was associated with the chiral factor of the external environment, and in ontogenesis - with the social factor. The sensitivity of the brain to these factors was based on the single-connected nature of its aqueous basis, the mechanism of electromagnetic induction, and the features of the thermodynamics of the brain in a state of night sleep. In order to unify the description of the mechanism of electromagnetic processes in the brain, the concept of a quasiphoton has been introduced, combining all forms of excitation of electronic and molecular-cellular structures of the brain. Equivalent schemes of vibrational contours of neural network elements and macrostructures of the brain are proposed. Estimates of the kinetic parameters (activatio...
MATHEMATICAL MODELLING OF NEURONAL PROCESSES
\ry¡THIN THE OCULCF'4OTOR 5 YSTEM. F.del Po:o; 5endra,A.; Delgado-Gancfa (l) ¡. m. and Portaen-casa. R. ; ';:' , Dept. ¿elc¡Uernética y Anállsis Numéricc. Facultad de ¡formÉ t¡ca., Ur\¡ 'ersidad Polltécnica. Ñ1adr^id. S pain. '] í (f). Oupl. Flsiologfa. Facultad de N4edictra'Sevllla.SFr¿ln' l Ab:tracl I '!" tt ls preser ted a mathemali:al model of the ocu notor plan! bas, d on experimer{tal data in cats' The 'stem that gener tesr, fnom thcil:uronal processes the mo¡oneuror tiie control sitrats to the eye muses that moves t¡ r eye. ¡n contltst with previous mo-ls, that base t:-eye movemell'''elated motoneuron 'havior on a fir order linean J'fferentlal equatlonr ''' ¡n-linear effect are descrlbedi A dependency onthe-e angular.posi r n of the modet "arametens..-. "-! .:' !t"d*!l'ln: Any appnc. ;h to lho sludi of tho braln is inten-C to undenesla' f lnally how the braln processes i4-matlort, whlct-. wo üs€umo, lp he subslnate fon be-¡vlor. And deo' g wlth lnlormLt on-pnocesslng sys-rrs, tlre theore' ;al wonk baseb >n slgnal theony, ln-matlbn thoory rd contnol systom theony, seems the equale framew'. k. lo covon ;that obiet¡ve. S¡nce the .' sslbllltles of e'v mathemallcaf modelllng tool' to ovlde a quantlt. :lve doscnlptlon of fenomena, how-en, relay on ltr,'oclequateknovvlodgo of tho neunonal .'tworks of lnter, st (the conneitlon between nervo-its) and the 6ct rlty of tho cells lnvolved; our efforls e tobe focusedc¡ the lhos€ noural systems both func-.,n8l-anatomlca.ly ldentlflod and wllh most of thelr-cults scceslbr' to oxplonatlc¡n.rmeasuFement of cell tlvl ty). I Tlre oculo" otor systom, ift. System lflat conlrols ,e niovementsr .,')poars as one of the maln candidates, .nslberlns that '6 funct¡on6l gnd conceptual featu-r l1 clearly p''ovldequtto enough system descriP-vr; ánd slgnal r''easurernenls al'" posslble since the. cent improveme 'lt of ths lechnlcues for reconding e sctlvlty of fur :llonal and anótrmlcally lderrtJfled rgloinounon¡ lr' olsrt bohovtng .nlmal¡ 6r l0' I l. ,q8 2 f... .r r.:.1 image fi>ed in the retina during head movementnEyo compensation control slgnals, to hold eye position ln spóce, ane generated fr.rm the semtcinculan canals that sense head acceleratlor¡.' Z) tne optokinetic subsysterr¡ that comptements the pnc vious orre, ünde;-slow move-rnents wlrene the canals do not oPerate (onnectlyr and uses the image slip as tl"e error signal.lhal genenates ihe control comrnands,¡ 3l The paccadlc subsystem whose purpose is to reorient the eye quickly in sPace to miniml2e the inte¡val duning whlch visior¡ ls lost.'4) The vergence subsyste?r wilh the alm of adapling bln-ocular vision to targets et different dlsrarices'; and 5) :l-he smooth Pursuit subsystem intended to inack mo-v¡ng targel with smooth ?ye movem?tt!:.'.''l *his communlcarion Is; llmlied lc an ¡mporlant pant of tre oculomoton system, ln tenms of neuronal lnfonmat'on pnocesslngr wh¡ch ls the cormon flnal 6tnucture of all the flve suboystensménioned:'Tho ocg lomotoF plant; that ls, the system that p-ocess tho slg-nals lnansmrnitetd and conditloned by'.tho dlfferent subsystems lnvolved ln any typo of 6ye movlmentr genera-tlng the control signal to lha eyo muscle tl'rat flnally rnoves lhe eye,''A furthe' llmltatlon of tte jwork here presented ls that only. the honlzonlál ,co pPnent of ths eye rnovement ls consldened for modellllg ipurposos. Restr'ictlon,which ts founcled on anatoml:at and fr¡nc-tlonal basésr since there are ,a:palr bf 'ryó muscles acting on the horlzontal pl6ne lnnervalbd r,y.a sPeclallzec class of motoneurons:abduceng neurona.' I A ¡nodel cf the oculomotor plant. 3lnce tl-¡s tntrocuctlon of the ter 'rntques of sln-gle neut on recordlng ln alert bohavlng ;nimals, lhe ey related ¡ehavlor of the rbducens motonc unons has beer extensir ely sludled,' Af sr a f irst stage of. qualltatlve descrip ionsr'ln l9?0 a nrothemallcil model, due simul-ta¡reous y-to Fucl'¡s and Luschel 4, Robtnsoñ 9 and Schille¡ l3' *uu publlshed.'tt relal'es the'mÓtoneurql dischar?e rate R(t) in spikes t-q"-t "o ! tne eye-lns' tsntane"us angular poslrlon e(t) ln deg'eés (posltlvo ln ths p¿lllng dlroctton of lhs:mu¡cls-tlp óo csllod or¡-dlns;tlon) moasunod ln the plano of '''ctlon of tho musclo :hat the cell belng constderod lneivatos.'The model l:; a flns! onden constant coefflclqnt llneer dlf-ferenti¡.1 equat¡on as fol lows: al ll'tax , fuuio , iwltr,¡n rt oculámotor sys em, f ¡ve mayor sub-ste4s can be cr..rtllned for lhose specles wlth a stnuc-e slmllan to t-.at of man: l) Tf : vestlbulo-ocular flexisubsyster'l, fhat altows ¡o naintaln the vÍsual I .l I
2007
This thesis formulates and evaluates a mathematical model from an engineer's point of view based on the currently-known information-processing processes and structures of biological neurons. The specification and evaluation of the RealNeuron model form a baseline for current use in engineering solutions and future developments. v vi I am thankful to all my teachers, mentors, colleagues and friends who have a part in forming me into the engineer I am today. I am very grateful to my parents for their love and support through all these years of study. Only after having children of my own, can I fully appreciate the sacrifices made by them throughout the years. My sincerest thanks go to my wife Ronel, and daughters Malisa and Bernice for their love, support, encouragement, sacrifices and understanding throughout my research. They are my spiritual support team for big projects. Everyday I experience the omnipresence of Elohim. He looks after me, guides me, and gives me the inner strength to continue my work; Sola Gratia. He lovingly reveals his own workings and works to me. He allows me to ask the difficult questions, to challenge current beliefs and to enrich my own personal relationship with Him; Soli Deo Gloria.
Mathematical models in neuroscience
In this paper, several mathematical models in neuroscience are provided. Firstly, the semantic system of the brain and the semantic logic, i.e., the logic that is specific to the human brain are discussed. Then, a mathematical-physical model to explain the mirror neurons paradigm is developed. Precisely, considering that any biological structure can be assimilated to a fractal (both structurally and functionally), a mathematical-physical model is proposed in order to explain the mirror neurons paradigm. Extending de Broglie's idea concerning the wave-corpuscle duality by means of the information (in its implicit and explicit) form, we are lead to assume the existence of a fractal medium, which can store and transmit information in the form of a natural field (called a fractal field). In consequence, the mirror neurons transmitting mechanism can be explained by this spontaneous symmetry breaking, in which the specific neuronal network and specific logics appear.
Editorial for Special Issue on Neurodynamics
The Journal of Mathematical Neuroscience, 2013
Neurodynamics" is an interdisciplinary area of mathematics where dynamical systems theory (deterministic and stochastic) is the primary tool for elucidating the fundamental mechanisms responsible for the behaviour of neural systems (whether biological or synthetic). A meeting on this topic was held at the International Centre for Mathematical Sciences in Edinburgh from March 5-7 in 2012. In this special issue, we have invited seven of the main contributors to this event to expand on their presentations and highlight the use of mathematics in understanding the dynamics of neural systems.
The Neuron: The Basis for Processing and Propagation of Information in The Nervous System
NeuroQuantology, 2010
Nerve cells (neurons) are the basis for processing and propagation of information in the nervous system. The morphology of neurons, the generation of action potentials and intercellular communication by chemical and electrotonic synapses is described and illustrated by schematic and electron microscopic pictures. Neurotransmitters are described according to their mode of action and the action of pharmaceutics and drugs on receptors and neurotransmitter levels is explained.
Biological Neuronal Networks, Modeling of
In recent decades, since the seminal work of AL Hodgkin and AF Huxley (1), the study of the dynamical phenomena emerging in a network of biological neurons has been approached by means of mathematical descriptions, computer simulations (2, 3), and neuromorphic electronic hardware implementations (4). Several models1 have been proposed in the literature, and a large class of them share similar qualitative features.