THE BRAIN

 

The nervous system

The nervous system has been divided by scholars into central and peripheral nervous system. The central nervous system includes the encephalon, inside the skull, and the spinal cord, inside the vertebral canal. The encephalon is made up of brain, cerebellum and brainstem. The brain is made up of its hemispheres, left and right, and the diencephalon, which includes the thalamus. The human being presents bilateral symmetry, so the right and left sides are like mirror images of each other. The hemispheres are made up of the brain cortex, on its surface, and the basal ganglia underneath. The brain accounts for two per cent of the body weight, but it consumes twenty five per cent of the available energy, so its metabolic activity is more intense than that of the rest of the body. Glucose (sugar) is the main source of energy for the brain. The peripheral nervous system, outside the central nervous system, is made up of nerves and nerve ganglia. The nerves are macroscopic cords formed by the neurites (axons and dendrites), which are microscopic filamentous extensions of the neurons. Some neurites can be up to a meter long (sciatic nerve), although still microscopic. The ganglia are groups of neurons. The nervous system generates responses through the integration of behaviours. The motor responses by the nervous system will be called behaviours here, instead of conducts, as the biologist Antonio José Osuna Mascaró, author of: “El error del pavo inglés”, advised me on one occasion. With a little luck the responses will be fast enough so that the behaviour helps to survive in a manner adapted to the environment; the fact that the brain processes various lines of thinking in parallel and at the same time helps to make that process faster too. Perception is the interpretation of the sensory information. For instance: If a person is watching a red snooker ball, the varied sensory information processed in parallel about shape, colour, etc., will be associated and integrated to perceive one sole object, a red snooker ball.

The basic cortical algorithm

The encephalon is made up of some one hundred thousand million of these cells called “neurons”. There are, besides, ten glial cells, support cells, for every neuron. Neurons and glial cells constitute the neural tissue. Different types of neurons have been described, but they all do the same, basically: Fire action potentials. The brain is roughly a small number of types of cells repeating the same patterns of activity (firing action potentials) a huge number of times, especially on its surface, the brain cortex. This was concluded as soon as 1897, by investigators like Meynert. All the neurons do basically the same: Generate, conduct and transmit trains of action potentials, electric discharges, following distinct and different stereotyped patterns of action potentials, forming different codes like that, on the different circuits of neurons inside the brain. According to Mountcastle, around 1978, the structure of the brain cortex is roughly uniform in all areas, being the variations not as many as to justify the functional versatility of the brain based on those local differences only, such multiplicity of codes. This fact attracted the attention of Hawkins and Blakeslee, authors of “Sobre la inteligencia” (2005), according to whom the same operations are executed on every part of the cortex, with the same basic cortical algorithm: The movement of stereotyped patterns of trains of action potentials. This implies that the heterogeneity of the mind, the fact that a region of the brain can be coding the round shape of a red ball while another region of the brain can be simultaneously coding the red colour of a red ball, must rest in the different codes, in other words, in the different and stereotyped patterns of trains of action potentials that different sets of neurons specifically form in different regions of the brain, it must rest in the heterogeneity of the frequencies and patterns of discharge of the action potentials that are being fired in the different sets of neurons. This idea will be important to understand the neural mechanism of consciousness presented in the final chapter.

The brain cortex

According to the “Tratado de Histología”, by Bloom and Fawcett, there are some fourteen hundred thousand million neurons in the brain cortex. The main difference between the human brain cortex and other mammal’s cortex is the former’s relatively bigger amount of surface. The human brain cortex begins to form during the eighth week of embryonic life. The brain cortex is horizontally stratified, as described by Baillarger, around 1840. It shows a vertical organization too, also called “columnar”, investigated by Lorente de No, who, for instance, concluded that the distribution of the sensory information, the one originating in sensory organs (eye, ear, etc.), is partly innate and partly acquired. Fortunately, this does not clash with anyone’s intuitions about the matter. For instance: For any pediatrician it soon becomes obvious, and another useful tool for the physical examination of their patients, that a healthy newborn innately knows how to blink to instinctively protect the eyes when something approaches any baby’s face. This particular behaviour (and others) has had to be genetically imprinted in the neurons from the beginning. Stam and Straaten, for instance, have reviewed some of those differences between the genetic predispositions and the acquired dispositions in models of organization of neural networks (“Stam C. J., Straaten E. C. W. The organization of physiological Networks. Clinical Neurophysiology 2012; 123: 1067-87”). There are primary areas in the brain cortex. They are called “primary” because it is there that some brain cortex activity starts after being stimulated by some previous encephalic activity through their connections. For instance: The sensory primary areas are the place where neurons from the thalamus connect with the cortex. The thalamus is a region of the encephalon, placed underneath the cortex, in the diencephalon, and involved in the transmission and integration of sensory information. The primary areas connect with the secondary areas of the brain cortex and these with the association areas. In the association areas varied information converges, and there it gets associated, integrated and its interpretation finally takes place, systematically. For the most part the brain cortex is association cortex, the place where conscious perception has been seen to take place in the human brain (see next paragraph).

Telencephalization

Animal species that have not developed a brain cortex throughout evolution yet, but do already have a thalamus in their telencephalic pole (the distal part of the encephalon on its way up), use another region, like the thalamus, for example, as a brain cortex, although with less functional complexity. For instance: In animals without neocortex, like the raven, the nidopallium assumes some of those functions, as explained by A. J. Osuna Mascaró in his book “El error del pavo inglés” (pages: 211-212). In animal species that already do have a brain cortex, it assumes functions that belonged to subcortical structures like the thalamus in previous more simple and primitive versions of that species. The brain cortex also executes new functions, unexisting in subcortical structures, due to its bigger complexity, like the ability to speak with a rational language in the case of the human being, or the acquisition of conscience to distinguish between good and evil, etc. This process, by which new nerve structures, superimposed throughout evolution, assume new functions, is known as telencephalization. Old functions do not necessarily disappear, so rational thinking coexists, rather uncomfortably for the neighbour sometimes, with irrational thinking. Telencephalization probably explains why conscious perception has been experimentally located in the association brain cortex in human beings, using neuroimaging, in some laboratories (like in the experiments by Metzinger et al. in the MIT, in 2000, for instance). Functional magnetic resonance imaging and also positron emission tomography have been succesfully used for this purpose. Once the telencephalization is established, the brain cortex takes over various functions, connecting downwards with structures underneath, forming different closed loop circuits, some of them already well known, like the “circuit of Papez”, a part of the limbic system, which is a neural system (like a subsystem of the nervous system inside the encephalon) involved in the control of emotions, a circuit that also includes other regions of the encephalon, like the hippocampus, the fornix, the mammillary bodies and the thalamus, all of them networking like that.

Somatotopy

The sensory information from each body part is directed to a particular place in the brain cortex, point for point. This is called somatotopy. That distribution of the sensory information literally draws the shape of a human figure on the surface of the cortex, known as “Penfield’s homunculus”, which still is a helpful concept in clinical procedures (see, for instance: “Fontoira M et al. Pie caído secundario a meningioma supratentorial; a propósito de un caso. Revista de Ortopedia y Traumatología 2003; 47: 134-7”). The thalamus is midway between most of the sensory organs and the cortex and also shows somatotopy (mapping, which leads to spatial coding). The human body is also mapped on the thalamus, but, after all, if a neural circuit connected distally with some point on the skin also connects with a precise point of the thalamus it will specifically connect with that point. Connections are quite stable, so each point is the one to acquire that somatotopic character, a specific point of a map, a spatial coding. There is a somatotopic distribution in other parts of the encephalon, including the retina of the eye, but this distribution does not form a homunculus in the retina yet, as Manuel Fernández Bocos, author of the book “El misterio de la creación”, asked me to point out.

Transmission of action potentials

The brain is made up of neurons, the neurons are cells, their most remarkable activity consists of conducting along them and transmitting between them the action potentials they fire, which are transient electric discharges of their membranes (the outer cell layer). Transmission takes place in the synapses, where neurons connect with each other without touching, through the secretion of neurotransmitters (specific organic molecules) from the discharging neuron to the neuron to get discharged in response to the neurotransmitter (phasic response) or to the neuron that is going to change the frequency of discharge of its action potentials in response to the neurotransmitter if the discharge of the second neuron was already taking place (neuromodulation). All of this occurs in thousandths of a second (miliseconds). A neuron can produce dozens of action potentials per second that can be transmitted to other neurons, forming sequences or trains of action potentials, constituting synapse and circuit specific stereotyped patterns. The neurons build microscopic circuits and the circuits macroscopic networks and these super-networks, all of which can work as identifiable functional units. As already mentioned, a given network can be identified with neuroimaging as the place where some particular function takes place, like the coding of information about the colour of a red ball, or like consciousness itself.

The transmission of information in the brain is algorithmically organized, following patterns. For instance: Transmission occurs in one direction only in the synapse, which generates order despite being the brain a chaotic system. Chaos means complexity, and therefore the possibility of heterogeneity. Order is also generated in the brain by the fact that each neuron does not connect with every other neuron, only with a limited number of them, and in a stable form, producing a flow of information along the circuits and networks with sufficient stability for consciousness to become a phenomenon as durable as to be analyzed and discussed.

The discharge patterns of the action potentials transmitted between neurons in the synapses, and the codes they constitute, are heterogeneous inside the brain, first of all, because the entry of sensory information in the brain occurs through various sensory pathways that are, themselves, heterogeneous to begin with, different from each other in the first place. For instance: The sensory cells in the ear, specifically sensitive to sound and not to light, and the cells in the retina of the eye, specifically sensitive to light and not to sound, discharge their respective action potentials towards the brain in different patterns because they are, as a matter of fact, different types of sensory cells themselves to begin with; they have different structures and their resulting behaviour is different too. Sensory cells are neurons that have been modified throughout evolution, including their stimulus specificity, the fact that they show a low threshold of response for a particular type of stimulus, like light in the case of the retina cells, and a high threshold for the rest of the possible stimuli. It is fortunate that the brain activity that abstracts reality can be this heterogeneous, because the reality to be conscious of is heterogeneous too.

Specificity

In an A-B-C neural circuit, as an information channel, neuron A could be considered the sender, neuron B the receiver and the synapse A-B the channel of communication. An action potential from A becomes a signal to be detected by B as soon as B responds to that signal with its own action potential, and not to any other physical phenomenon that could act as a possible signal for B. This discrimination by B of the proper signal, this specific activation of B, has to do with the signal specificity of the receiver for that signal and not for another. The consequence of this is that the information the synapse transmits, although it does not identify with the stimulus (they are not the same object), it identifies the stimulus (represents it specifically) and consequently a congruent isomorphism can be established, as soon as a correlation between a given stimulus and a distinct discharge pattern in a circuit becomes exclusive. For instance: There will be one mental interpretation of redness only, so, although the visual appearance of redness is indescribable us such, once learned how to recognize it by trial error, during childhood, it will be hard to mistake it with any other thing, in general. It will not be easily mistaken with the timbre of a bassoon, or with the scent of cinnamon. So specificity is fundamental in the mental process and, hence, in the emergence of consciousness, as consciousness involves some specific type of mental information only, that part of the mind with the emergent properties of uniqueness and individuality.

Photoreceptors

There are two types of photoreceptor cells in the retina: Cones and rods. Rods are more sensitive to light so they are more useful for night vision. They get promptly saturated during daylight. Cones are better with intense light and to perceive colours. There is a different kind of pigment inside each type of retina cell. Each pigment reacts to a different light frequency. The frequency at which different photons vibrate reveals their particular energy level. The perception of different colours corresponds to the differences between light frequencies. There are four pigments: Rhodopsin inside the rods, and three different types of pigment inside the cones. The pigments are able to absorb energy in the form of photons. Light is energy, and at the current rank of temperatures of the Universe it has been found to be made up of a type of elementary particles called “photons”. Each pigment presents a peak of photon absorption for a certain level of energy. Rhodopsin shows a peak of absorption for the frequency of 496 nm (nanometers). That is the kind of photons rhodopsin absorbs more easily. The other three pigments show their peaks for 419 nm, 531 nm and 558 nm (Data supplied by Dartnell in the “Tratado de fisiología humana”, by Tresguerres) which correspond to violet, green-yellow and yellow colours. There are not colours in nature (or scents, or sounds), colours are emergent mental interpretations or conscious perceptions (Also called “qualia”) of the combinations of light frequencies within reach of the photoreceptors, once coded and sufficiently computed inside the brain.

The brain transduces different types of energy (sound, light, etc.) in the same type of energy, bioelectric, to efficiently compute all of that available and otherwise unusable information about the environment at the same time in the same place, the brain. Transduction (and abstraction) makes sound and light compatible for computational purposes and for evolutionary convenience, because survival of the species usually is at stake in this matter of the diverse physiological functions and their characteristics. The way to make sound and light compatible, when thinking about both, consists in the abstraction of the information they convey (information about frequencies). Light (electromagnetic energy) and sound (mechanical energy) get both transducted into biolectric discharges that generate patterns that become codes that specifically, congruently and isomorphically do represent (do abstract) those particular frequencies of sound and light that reach the system and the sensory cells are specifically sensitive to with a low threshold. Thus, the ability of the brain to compute abstract information is the trick to take advantage of some of that circumstantially useful information about the environment. We more or less understand that information, find it correct on the go, and that computing eventually works in healthy conditions, because there is sufficient automatic order inside the brain despite its chaotic complexity, there is sufficient specificity, congruence, ismorphism, relative simplicity due to the mechanisms of neural integration, and sufficient predictability (memory); and some coding and computing. Perception is the knowledge of reality from sensory information, its interpretation, the identification of the object of perception in the mind. The sound is perceived as sound and light as light not only because the codes are exclusive for each type of sensation, but because the neural codes are able to be different as well, for they originate in different sensory organs, the ears, the eyes, etc., which respond with different patterns of discharge. Sensory organs are specific, ears do not respond to light and eyes do not respond to sound, they are specific for certain stimuli and not for others. That is why the neurons specifically connected with the ears are useful to hear sounds and the neurons specifically connected with the eyes are useful to see light. As the sensory organs are different and their patterns of discharge too, the information to enter the brain is heterogeneous from the beginning. This poses an advantage because the reality the brain is about to compute about is heterogeneous too, so the brain is going to need to generate heterogeneous abstract information if it should be able to rebuild a representative image of the world sufficiently isomorphic and congruent with its macroscopic appearance, with its complexity and with its heterogeneity to help to succeed in the trials of life. Some amount of complexity and chaos, disguised as order, for instance, to perceive crocodiles instead of photons, can become an advantage in the end, if used properly, because crocodiles are agents of chaos too, so they are game.

Equifinality

An open thermodynamic system exchanges matter and energy with the outside of the system. The brain is an open system: It receives energy (glucose), oxidizes the glucose and emits energy (heat). In open systems the outcome does not depend on the initial conditions. What this means is that heterogeneity is possible for the brain as a computing system, so the brain is not necessarily going to integrate the same pattern of response for different stimuli, although it could (It does to some extent, as it is the case, for example, with stable reflex responses). “Equifinality” means that the outcome of a system will be the same final state although the initial conditions are different. The brain is capable of equifinality too, but that is not incompatible with its growing complexity throughout evolution and the natural selection of different outcomes according to convenience for survival and other factors, and it is not incompatible either with the heterogeneity of its actual activity. The brain is complex enough to be versatile and efficient, but not as complex as to be completely useless though.

Brain and evolution

Life defines living beings, those which, for instance, and according to the usual definitions, nourish, grow, reproduce and show specific purpose, sensitivity, motility and adaptability. As professor Ángel Berjón said, there is no life, only living beings, and for the same we should say that there is no consciousness, only conscious beings. The cell is the most basic structure able to sustain life. The first cells, the prokaryote, which formed in the sea thousands of millions of years ago, as soon as the earth cooled down sufficiently, did not have the genetic material inside a nucleus yet, like the later and less primitive versions, the eukaryote. The human body cells are eukaryote, with the genetic material in the form of chromosomes inside the nucleus. The genes are made of nucleic acids chains, a type of organic molecules, which code information to synthesize another type of organic molecules called proteins. The prokaryote are the bacteria. The eukaryote include protists (unicellular beings), fungus, metaphytes (pluricellular plants) and metazoans (pluricellular animals). The sea sponges are some of the first metazoans to appear and they still exist as of today; they show different types of cells, some cellular specialization then, but they do not form tissues and therefore neither organs yet. Sponges have neurons but these do not form circuits, chains of neurons, so they probably lack a mind (the ability to compute abstract information) and consciousness. More evolved metazoans form cell tissues when different groups of specialized cells separate from each other by a layer of an extracellular matrix, the so called “lamina basal”. If the embryo of an eumetazoan forms two layers of cells it is called diploblastic, if three, triploblastic. The jellyfish, for instance, is diploblastic and shows radial symmetry. The triblastic, on the other hand, have bilateral symmetry, with the exception of the starfish and some other echinoderms that have radial symmetry. The triblastic, with the exception of echinoderms, are interesting because they are characterized by the phenomenon of cephalization; in a nutshell: They have heads; a seeming advantage when searching for food by swimming. The mollusks, like squids or snails, are the first triblastic where clusters of neurons in the head, the neuronal ganglia in the cephalic pole, joined to form an organ to be called a brain. Octopuses, which are mollusks, even show creativity when solving problems, like the chimpanzees, or the parakeets do as well. A branch of the triblastic, the Craniata, not only have a head, but a skull in addition; they include the group of the lampreys and the group of the Vertebrata, the animals with a backbone. The Vertebrata (fish, amphibians, reptiles, birds and mammals) have head, trunk and tail. The tail is atrophied in humans and other animals. The head is made up of two symmetrical halves, the brain, too. The head is formed by skull and face, and it joins the trunk at the neck.

The characteristics of the human brain, its bigger relative size and its greater complexity, are a part of the result of the process of hominization, the evolutionary change that has led from a primitive primate to the current human being during the last forty million years. There is only one species to be named human at the present time, the Homo sapiens sapiens, and that is peculiar, because, while the word Homo refers to an animal genus, and a genus can be represented by several species, there is only one kind of human being nowadays. There were several human species in the past, some of them at the same time, but they are all extinct, except one. The fact that there is only one human species left today might not mean that the human kind is special, but sparse instead, and perhaps progressively less diverse, when diversity is a weapon for the survival of a species. The insectivorous are the group of mammals whose evolution drove to the primates, which in turn evolved into humans. Mankind is classified in the order Primate and the class Mammal. There are two branches of mammals: Placentals and marsupials. Placentals have a longer gestation and the ossification of their skulls begins later, what might have had to do with the fact that the skulls of the placentals, unlike that of the marsupials, have been able to become progressively bigger in size and content in some species, throughout their phylogenetic evolution (The word “phylogeny” concerns the genetic and consequent physical and behavioural differences between parents and offspring, while the word “ontogeny” refers to the changes any individual goes through along the embryonic development). Hence, convergent (with similar characteristics, like wings in birds and bats) placental and marsupial species, that by chance shared the same niche, ended their competition in some known cases with the survival of the placental and the extinction of the marsupial. For instance: Such scenario has been suspected by some paleontologists to have been the case between the placental Smilodon and the marsupial Thylacosmilus, perhaps due to the advantage the bigger brain of the placentals would have brought over its direct competitor. Hominids have relatively bigger brains too, so maybe longer gestations and later skull ossification have had to do with the fact that hominids and their relatively bigger brains have inevitably derived from placentals in the first place. Primates are mammals characterized, among others, by the next: Having five fingered hands, opposable thumb, pronosupination, nails instead of claws, being plantigrade, having frontal stereoscopic vision, being eutherians (placental) and having the rinopallium, the olfatory brain cortex, relatively less developed. The order Primate probably appeared during the Pliocene in the Tertiary Era, some sixty million years ago. Primates probably evolved from insectivorous mammals with an appearance similar to that of the still existing shrews, which were in turn descendants of primitive reptiles. The order Primate is usually divided into two suborders: Prosimii (lemurs, lorises and tarsiers) and Simii, also called Anthropoidea (Platyrrhine and Catarrhine). The Catarrhine appeared some fifty million years ago, during the Eocene, and have thirty two teeth, including the human adult. There are four families of Catarrhine: Cercopithecus, the only Catarrhine with a tail, although not prehensile, Hylobatidae, which are the gibons, including four species, apes or Anthropomorphous and hominids. Anthropomorphous “means human-like”, and it includes three genuses: Pongo or orangutan, Pan or chimpanzees, with two species, and Gorilla. Hominids show their differential characteristic, which is bipedalism. There are two genuses of hominids: Australopithecus, extinct, and Homo, mankind, still existing while this words are being written. Australopithecus were human-like and also chimpanzee-like, although probably unable to speak, as some paleontologists have suspected based on the fact that the size and morphology of their larynx would not have allowed the pronunciation of vowels. They included several species. There were several species of the genus Homo too, like Homo erectus, and Homo sapiens neanderthalensis, both extinct, and the still surviving Homo sapiens sapiens, a catarrhine ape, biped and without a tail, who constitutes the only surviving species of the Hominid family and the Homo genus.

Neoteny

How did a biped ape without a tail evolve into a member of the genus Homo, a human, a hominid with a disproportionately bigger brain? It could have been the result of the association of three biological phenomena: phylogeny, anthropomorphism and neoteny. Neoteny, consists in the manifestation of infantile or larval features in adulthood, like the relatively bigger heads children have, so, basically, it means to reach sexual maturity during the larva stage, as explained in the book “Elementos de biología”, by Planas Mestres, (page 347). Amphibians that keep their gill in the adult stage are a typical example of neoteny, like the species Proteus anguinus. I searched to find out who else had come to the same conclusion and found that Lamotte, in his book, “Antropología neuroevolutiva”, had already written that humanity owes its appearance to neoteny. Lamotte learned it from another book, by Changeaux, “El hombre neuronal”, who had found the idea in a paper by Bolk dated in 1926. Bolk had mentioned how similar a young chimpanzee was to an adult human, as if fetal features persisted in humans during adulthood, for instance, the characteristic smaller human superciliary arch. Bolk noted that the adult human skull looked like the skull of the fetus of a chimpanzee and hence man would be a neotenic animal. Thus, perhaps the progressive relative increase in human skull size throughout phylogeny, including container and content, compared to previous primates, would have had to do with a tendency to maintain infantile proportions during adulthood. The increase of the content, that is to say, the increase of the total number of neurons, has been even bigger than the increase of the total volume of the skull, as Mora reminded in his book: “Continuum ¿cómo funciona el cerebro?” The structure of the brain in the form of folds and convolutions, the progressive increase of the brain’s total surface in this manner throughout human evolution, has made it possible for the brain to fit inside a skull whose volume has not been increasing at the same rate throughout phylogeny. Anyhow, the increase in the number of neurons, the neuronal hyperplasia, frequently drives to megalencephaly, a pathological condition, as, for instance, reviewed by Bruner (“Bruner E. et al. Functional craniology and brain evolution: from paleontology to biomedicine. Frontiers in Neuroanatomy 2014”).

The neuronal theory

The body cells carry out four types of functions: mechanical, osmotic, chemical and electric. The neurons, which are cells of the nervous system, show a remarkable electric activity (Some cardiac cells that act as pacemakers too), as they continuously generate, conduct along their surface and transmit to other neurons electric impulses that reach the muscles and other organs. Muscle cells, on their part, have the ability to respond to those impulses and conduct them along their surface as well, to accomplish the muscle contraction and integrate the motor behaviour. The neurons generate those bioelectric impulses carrying out an electric discharge, through a transient change in the electric charge of each neuron between both sides of their external membrane. That impulse is conducted along the cell membrane and transmitted from one neuron to the next through the unions between neurons, the synapses. According to the “neuronal theory” the brain is formed by neurons. Neurons connect with each other through the synapse, a type of specialized cellular union. There are several types of known specialized cellular unions, as described by citology and molecular biology, types of connections between cells at the molecular level to exchange substances; the synapse is one of them. The discovery of the neuronal theory is usually placed around the year 1888, although it was developed step by step. Wilhelm His had brought about some fundamentals in 1887, when he observed the growth of neuroblasts, precursor cells of the neurons (immature neurons). The neuronal theory opposed to the reticular theory by Gerlach (1858), eventually proved wrong. The reticular theory stated a structural continuity of the nervous tissue, so it would be like a wired continuous telephonic network. It happened not to be so: Surprisingly enough, given that conscious perception seems to be a continuous experience at first sight, it was later discovered that the neurons do not get in touch with each other in the synapses. Ramón y Cajal did the most for the neuronal theory. He used the knowledge accumulated by previous researchers, and contributed himself technical innovations and the results of observations and personal interpretations, commonly characterized by being ahead of his time. His observations proved the reticular theory wrong. The neuronal theory was proved correct, time and again, in the following years after Cajal’s work. The discovery of the electronic microscope was crucial to prove that the neurons were truly cells, individual structures, and separated from each other by a spatial gap in the synapses. According to Cajal’s neuronal theory: In the nervous system the functional unit is the nerve cell, and not a continuous network; the neurons connect with each other through specific intercellular unions, the synapses; and the electric current reaches the neuron through its dendrites and leaves the neuron through its axon by means of a “dynamic polarization” in the synapses (a one-way transmission). The first known mention of the nerve cell was made by Dutrochet in 1824. He called them “globular corpuscles” and identified them as the origin of some neural energy that the nerve fibers, which had been previously described, should be conducting. Later on it would be found out that that nervous energy was bioelectric. Deiters described more clearly the parts of the neuron around 1825: The soma or cell body and its prolongations, the neurites, axons and dendrites, with their characteristic arborescent ramifications, following a fractal pattern (so common in nature on different scales, like other common patterns, such as the swirl, or the sphere). Dendrites had been described by Valentin in the middle of the nineteenth century. The axon, the solitary prolongation with a ramification too in its distal end, had been described by Fontana in 1781. Waldeyer gave its name to the neuron in 1890. Galvani was aware of the electric properties of what the neurons did around 1780. Galeno, around the second century, guessed that whatever it was that the nerves were doing it had to be two-way: sensitive and motor.

The physiology of the neuron is more complex on a molecular level than that of the rest of the body cells, as Cardinali explained in the Tresguerres’ textbook on human physiology. There are several classifications of the neurons regarding their anatomic features or molecular specifications. For this reason, different types of neurons are known. The differences between the types of neurons shown in these classifications do not hide that all of them are doing more or less a same common thing: Generating, conducting and transmitting action potentials, electric discharges, and frequently in a modulated fashion, with changes in the frequency of the individual discharges. Apart of modulation, neurotransmitters have other functional possibilities, for instance: Serotonine can also act in a phasic fashion, briefly, and not only as a neuromodulator, and it can also act on both sides of the synapse, and this referred only to the case of the serotonine and summing up only some of its features, but there are many other complex details in the brain anatomy and physiology once it is thoroughly observed at close range, although it can also be unmistakably discussed by means of more general ideas. The differences between the types of neurons that have been found are not as many as to explain the versatility of data processing in the brain. Ramón y Cajal wrote about it in 1899, in his textbook on Histology: “El tamaño y disposición de las células nerviosas, así como el de sus expansiones, no parece referirse de un modo evidente a determinada modalidad funcional… (“The size and disposition of the nerve cells, as well as their expansions, do not seem to evidently concern any functional modality”)”. In other words, and for instance: Neurons connected with the ear are useful to hear sounds not because they are specialized in hearing sounds, but because they are connected with the ear, which is sensitive and reacts specifically to sound, not to other types of sensory stimuli.

Biophysics

Du Bois Reymond developed biophysics as a branch of biology dedicated to investigate the bioelectric properties of the neurons and their action potentials. The goal of physics is to measure. From the point of view of biophysics the neural bioelectric activity can be explained with concepts from classical physics, like conductance, electromotive force, capacitance, etc. This has been carried out in various manners, in different parts of the world, for decades, for instance, through the electrophysiological research of ion channels, the selective pores of the cell membranes. Du Bois Reymond found out that the neurons conduct the electric current they generate along their prolongations around 1848. They generate that current through an electric discharge across their membranes, the action potential, which Cardinali called “cambio eléctrico transitorio” (“transient electric change”), a discharge powerful enough to propagate along the neuronal membrane as an electric current. Neurons conduct and transmit the electric impulses they generate with their discharges; conduction and transmission are not the same; conduction takes place along the neuronal cell membrane; transmission occurs between two neurons, jumping the gap between them, where axons and dendrites of each two neurons connect with each other without touching, the synapse. The transmission of electric impulses between neurons follows a mandatory one-way path, the “dynamic polarization”, from the presynaptic pole, where the axon sends the stimulus, to the postsynaptic pole, where the dendrites of the next neuron receive the stimulus, to conduct it again and transmit it to the following neurons of the circuitry.

The action potential

Neurons, like the rest of the cells, are delimited by their cell membranes, a lipid bilayer, delimited but not limited, because the membrane is a part of the neuron. The membrane is semipermeable, it has selective permeability: Certain ions are allowed through their selective pores (channels) sometimes in one way or another, and other times they are not, depending on whether or not the channels are more or less specifically open or closed for them, as the pores respond to their specific stimuli. The ions taking part in the generation of electric potentials in the neuronal membranes are mainly sodium and potassium, with an implication of others, like calcium, magnesium and protein ions. That semipermeability leads to an asimetric concentration of the different positive and negative ions on both sides of the membrane and therefore to an asimetric electric charge inside and outside the neuron, negative on one side and positive on the other side of the membrane; that is what charges the membrane, the asymmetric concentration of opposite electric charge ions on both sides of the membrane, and that is what can give rise to its discharge and therefore its electrical activity, due to the attraction between positive and negative ions, when the proper channels open and some of the ions flow in a given direction, changing the charge on both sides and therefore discharging the previous charge. As the electric charge will be different on both sides of the membrane, the neurons get charged by this electric potential difference on both sides. This asymmetric distribution of the electric charges, this transmembrane electric charge, with its corresponding potential difference, or voltage, is partly accomplished in a passive mode, as certain pores stay open and some of the ions go through them freely in some phases of the process, but it becomes active in other phases of the process of charge/discharge. When active, the neuron pumps some specific ion in one particular direction, across the membrane, depending on the phase of the process, and it does so actively, that is to say, consuming some of the energy (originally glucose) stored inside of the neuron. The electric discharge of a neuron can be spontaneous or evoked. The body cells able to discharge spontaneously may act as pacemakers, like some cardiac cells and some neurons do. The body cells able to get discharged are called “excitable cells” and these are the receptor, the nerve, the muscle and the glandular cells. The excitable cells, after getting discharged, respond with some cellular behaviour, which cannot be categorized as conscious, but as purposeful, as it will seem to have determination, like nourishing, despite the lack of consciousness (and free will) of any single cell. Purposeful and conscious are not synonymous then. The response to that discharge can be a contraction, as is the case of the muscle cells, or it can be a secretion, like the neurons do. Neurons secrete neurotransmitters (not consciousness) across the synapses. Neuronal excitability makes the sensitivity of the nervous system possible, the capability to react and therefore respond to stimuli. Excitability and sensitivity are crucial for consciousness too.

The resting membrane bioelectric potential theory was introduced by Bernstein in the beginning of the twentieth century. The cells show a resting potential, a different electric charge on both sides of their membranes before getting discharged. It took fifty years, from the early experiments by Overton around 1902 until the late findings by Hodgkin, Huxley, Katz and other investigators, towards 1952, to sufficiently clear out the ionic theory of action potentials, the mechanism of ion flow on both directions of the membrane linked with the detection of the different types of potentials to be detected during the process of charge-discharge, like the resting potential or the action potential. The neuronal action potential becomes detectable when neurons get discharged. During a discharge of an action potential, once it starts in a zone of the membrane it will conduct along the surrounding cell membrane surface. The synaptic transmission is performed action potential by action potential, something that remotely ressembles the 0/1 informatic system. The structure of the synapse implies that not only the action potentials are transmitted one by one, but in one direction only as well. These anatomic and functional structure establishes a tendency towards order in the system even on the neuronal level, an algorithmic structure already at the microscopic level, even though the brain is basically a chaotic system. Therefore it is not unthinkable that this particular functional structure of well established pathways has been able to lead to a computing system, an organ capable of thinking. The “all or nothing” law is followed. According to the “all or nothing” law, whenever a stimulus is intense enough to evoke an action potential, it will appear in every case and to its possible highest amplitude (voltage). That maximum voltage will not be identical in the successive action potentials of a single neuron, but it will be the highest voltage possible for each single action potential, as it happens when someone jumps up with a maximum effort, reaching a maximum possible height in every jump, but slightly different heights with every jump. If a stimulus is as intense as to evoke a membrane depolarization able to cross the threshold for the discharge of an action potential, it will appear on the point of discharge and propagate the discharge process along the rest of the membrane until the synapse on the tip of the axon, where a proportional amount of neurotransmitter will be unloaded across the gap to reach the postsynaptic membrane of the next neuron, where a new action potential will be evoked if the amount of neurotransmitter is enough. Multiple neurons converge on each postsynaptic region, as each neuron establishes synapses with thousands of other neurons at the same time, so postsynaptic responses are as probable on the microscopic scale as to easily guarantee an efficiently enduring thought process on a macroscopic scale, despite the fundamental chaos. The “all or nothing” law is followed due to the properties of the membrane concerning permeability and ion diffusion, according to voltage and ion concentration on each side of the membrane, and because energy is consumed for that purpose, to reach that possibility of firing an action potential. Energy is stored inside the cell in the chemical bondings of some molecules, like ATP, adenosine thiphosphate. The “all or nothing” is prepared before the action potential is delivered by consuming the energy in the ATP in order to actively charge the membrane, bombing certain ions in one direction (bombing positive ions outwards), creating a negative charge inside the membrane and positive outside the membrane during the resting potential, until reaching an equilibrium potential. That equilibrium, as every other equilibrium in nature, is unstable. It consists in an asimmetry mantained through an active bombing. However, negative and possitive ions attract each other strongly, so, as soon as the specific pores are open by the proper stimulus, the equilibrium will be broken (There are specific stimulus for different types of specific detectors: Mechanical, like in sensory touch detectors in the skin, electromagnetic radiation like photons for the retina, chemical for the tongue, etc.). That equilibrium, with electronegativity inside and electropositivity outside, represents a potential difference on both sides of the membrane, as there are more negative charges inside and more postive charges outside. That difference mantains an unstable equilibrium within a range of milivolts (thousandths of a volt) through that active bombing, plus some passive diffusion on both sides and some selective blocking of pores, until an action potential occurs. Thus, there is a potential difference on both sides of the membrane during the resting potential, a different electric charge on both sides, negative inside and positive outside. It is called “resting potential” because the action potential is “at rest”, although it is mantained actively (restlessly). The breakdown of this situation will be a discharge, when a specific stimulus opens the specific ion channels, and a passive flux of positive ions inside starts, attracted by their mutual opposite charge (negative and positive charges attract each other). The sudden pasive flux, bigger than the active flux, will tend to even their concentrations on both sides on that point of the membrane, until their minimum energy state has been reached by the end of a single discharge. In the neurons the electrical conduction consists of the movement of ions across the membrane, not along the membrane, although the current propagates along the membrane, when the local discharge opens the nearby pores and the action potential spreads. If a discharge crosses a certain threshold it will become an action potential, an all or nothing discharge, until the potential difference passively vanishes for itself. In nature, at this point in the evolution of cells, the potential difference during the resting potential is already as wide as to be able to reach that threshold once the depolarization (the discharge) has started. Once the threshold is crossed the flux of ions will be intense enough to be unstoppable, like it does when a dump truck of a gravel truck is tilted with sufficient slope so the dumping becomes spontaneous and unstoppable when gravity exceeds friction, and that is what that all or nothing of the action potential is about too. The optimum unstable equilibrium so that an action potential can be fired is a charge of around -85 mV (milivolts) inside the membrane. That charge makes the attraction between the negative charges inside and the positive charges outside irresistible, like gravity exceeding the friction between the gravel grains, so once the pores are unblocked by some specific stimulus the action potential will be fast and unstoppable (the positive ions will “fall down” inside) despite the active bombing in the opposite direction. The threshold from which the depolarization is unstoppable and the action potential will be fired all or nothing is around -45 mV. The unstable equilibrium during the resting potential is easily broken in nature and the action potential occurs down gradient, so it does not need to use energy of the ATP, it rolls down its particular tilted hill on its own towards its state of minimum energy, the bottom of the hill. A “gradient” is a vector, an arrow, that helps to give a vector measurement of the change of a magnitude in a system and informs both of its direction and intensity.

Facilitation

The action potential is produced by an alteration of the ion equilibrium on both sides of the membrane, spontaneously, as in pacemaker cells, or in response to a stimulus, like it happens when a neurotransmitter selectively opens some specific ion channels in the postsynaptic membrane. It is a depolarization of a previously polarized membrane. Once depolarized it will be polarized again, repolarized, using ATP. Few ions are needed for each action potential though, so the process is not so costly after all, it is quite affordable for the cells. If a neuron is partially depolarized, without reaching the threshold, for instance, by the addition of some stimuli converging on it, its potential difference can be close to those -45 mV. In that situation the neuron would be partially discharged. An action potential will be fired more easily in that case. That is called neuronal facilitation. Before two computing options in a diverging neural circuit, facilitation makes one of both paths more probable to be taken by the successive neural impulses, normally the more facilitated pathway. The concepts of diverging circuits and facilitation connect with the ideas of choices, freedom to choose and free will: In the brain, as a computing system built on circuitry, free will possibly has more to do, on the microscopic scale, with having more than one option, with the existence of more or less facilitated crossroads to follow with more or less probability, with what one can automatically think of, on a microscopic level, rather than with what one illusorily feels that voluntarily wishes to think of on a macroscopic level at first sight.

Inhibitory synapses, hyperpolarization

Some stimuli partially depolarize or facilitate some neural pathways for different reasons, but, curiously enough, a majority of the feedback stimuli in charge of facilitation are inhibitors, not exciters. The description of the inhibitory synapses owes much to the research by Eccles. The inhibitory feedback circuits become excitatory with a trick: Using two successive inhibitions, because the inhibition of an inhibition becomes an excitation. Inhibitory synapses are a fundamental regulation mechanism in the brain (Regulation means unconscious adjustment of a system, while control means conscious adjustment). The inhibitory synapses cause an opposite effect to the excitatory: They move the neuron away from the action potential discharge threshold, -45 mV, setting the polarity even further than before, in -90 mV, for instance, a state called hyperpolarization, in which the neuronal response to a stimulus is worse than before, so the stimulus will have to be more intense now.

The synapse

The term “synapse” was coined by Sherrington in 1897; it means “union”. The synapse is a type of specialized intercellular union, of which there are several types known. They are molecular structures in the membrane that work as a bridge between cells. Ramón y Cajal found out that the connection between neurons in the synapse takes place: “… en contigüidad, no en continuidad… (“in contiguity not in continuity”)”, without touching. There are different types of synapses; we are referring here to the most common type in the human brain, mediated by this type of neurotransmitters, like serotonine and others. Other types of synapses, like electric synapses, are so rare in the human body that they can be considered “ephapsis” (false synapses) and pathological in the first place (see, for instance: “Seijo M. Fontoira M, et al. Myoclonus of peripheral origin: case secondary to a digital nerve lesion. Movement disorders 2002; 16: 970-4. PMID: 11746636”). Already at the time of Du Bois Reymond the secretion of a chemical mediator from the presynaptic membrane to the postsynaptic membrane was suspected to be involved at the transmission in the synapse, an organic molecule that would alter the transmembrane ionic flux and provoke a new action potential in the target. The first explanations were supplied by Vulpain around 1866, pursuing the previous research by Claude Bernard around 1857. The chemical substances that jump the gap in the synapses were called neurotransmitters and dozens have been identified until today. Elliot, among others, started to make it clear that neurotransmitters were responsible for the transmission of the action potential between neurons around 1904. Each neuron can establish some ten thousand synapses with other surrounding ones. This means that some three hundred millions of millions of synapses are possible for a brain. Each synapse can discharge some fifty action potentials per second (50 hertz or Hz). This gives a glimpse of the magnitude of the information a brain can process, because the synapse is the place where the coding of the mental information probably takes place, as it is the localization of the dynamic polarization of specific and distinct patterns of discharge. In the synapse an order is established to the flux of information, an algorithmic structure. Not only that one-way transmission provides congruence, but the fact that each neuron connects with some other neurons but not with all the other existing neurons, and with fine feedback regulation systems, provides congruence too, because it makes each transmission to have a particular shape and to be moved along a specific pathway and therefore to have a meaning. Neural plasticity consists of the ability to modify the molecular structure of the synapse, adapting it to the changing functional demands. It happens throughout life, because synapses systematically grow, mature and degenerate (the synapses, not the neurons), some strengthen, while others weaken, some are formed new and others disappear. These changes in the molecular structure of the synapses were investigated by Hebb and are related to the types of memory: Short term and long term.

Neuronal integration

Sensory processing towards the goal of perception and consciousness includes the association (parallel processing), integration (addition) and computing (thinking) of the mental abstract (representative) information inside the brain. Integration is key to behavioural success, but it has a price to be payed: The loss of temporal or spatial resolution, or both, because integration carries a loss of detail, for instance, during the process of perception (like when we perceive red balls instead of photons). Curiously enough, that loss of resolution became key to the lucky phylogenetic developement of these neural mechanisms, because that loss of resolution happened to be compatible with the way the macroscopic world we inhabit should be perceived, so as to be aware of it in a congruent manner. Some neuronal integration mechanisms are known, for instance: If two neurons, A and B, connect, both, with a third neuron, C, forming a convergent circuit in Y, then C will integrate the activity of A and B. Thus, there are convergent neuronal circuits like that, or divergent circuits, when A connects with B and C.

Different types of connections will have different functions with these types of circuits, like enhancing the contrast of the sensory signal through lateral inhibition of impulses, or like increasing the intensity of a given signal through neuronal recruitment, etc. Another mechanism of integration involves a direct circuit, like A-B-C, in which B integrates A and C. This type of intermediary or inserted neurons between stimulus and response, like B, are called “internuncial neurons”. There were not internuncial neurons in the primitive nerve tissues, like the sea sponge’s. Sponges possibly did not have circuits back then yet and do not yet as of today either, because they are not extinct. Twenty per cent of the brain cortex neurons are internuncial, small inhibitory neurons; the rest, the pyramidal neurons, are excitatory. The activity of inhibitory neurons is local at short distance, because they have short axons. Neural integration mechanisms inside the brain weave complex circuits and networks that present a property called metastability, an equilibrium through different states (states of integration, in the case of the brain). Metastability in the brain grants that it can be successful in its outcomes, even in basic but necessarily succesful for the survival of certain species responses, like the coordination of both eyes when looking left or right at the same time.

Neuronal synchronization

Neuronal synchronization mechanisms are considered neuronal integration mechanisms too, as reminded by Paula Postigo in her doctoral thesis, where she cited some articles on the matter by Lopes da Silva. Neuronal synchronization is considered a mechanism of neuronal integration, because it allows the explanation of the functional connection between neural sets (“Lopes Da Silva, F., 2013, EEG and MEG: Relevance to Neuroscience, Neuron 80/5, pp. 1112-28”). This idea will be important to explain the possible neural mechanism of consciousness in the final chapter. Strogatz et al found out that any system of coupled oscillators, interconnected and with characteristic frequencies, such as the neurons, spontaneously self organizes and the result is synchronization (“Strogatz, S. H. et al, 1989, Colletive dynamics of coupled oscillators with random pinning, Physica D: Nonlinear Phenomena 36; 1-2/36, pp. 23-50”). Self organization and periodicity resulting in synchronization are possible for the brain, being it a negentropic and open system (see chapter 4). The brain is an open system, so order is possible. That includes periodicity phenomena, like neuronal periodic oscillations between charge and discharge; as a matter of fact neurons are considered coupled oscillators able to synchronize.

A wave is a disturbance transmitted in a medium or in the vacuum. A wave motion consists in the transmission of a simple harmonic vibratory motion, a periodic oscillation around a point zero, with variable speed, which is proportional to the distance to that point, like in a pendulum. It can be represented with an undulated line, with time in abscissa (the horizontal coordinate), to visualize the period, which is the time between, for instance, two spikes of the wave, and the frequency, which is the number of, for instance, spikes of the wave per second (The spikes are the highest points of the undulated line, the tips. A phase is any concrete point along the line. The spikes, for instance, are phases and as they are easier to visualize they will be used in the following examples to expose some concepts). The distance between the same successive phases of the same wave, between the spikes, for instance, is known as phase difference. If the wave is regular the phase difference will be constant, a constant amount of time. Given two waves there will be a phase difference between them too. If both are regular their phase difference will be constant too. When two regular waves coincide by all of their spikes a synchronization of their frequencies has taken place. For this to happen both waves must have the same frequency. If the two waves synchronize their frequencies the phase difference between them will be constant. A constant phase difference between two waves with the same frequency will suppose a coupling of the oscillations of both as a function of time, a synchronization, and all of their mutual spikes will coincide on the same points along the timeline. The same thing will occur with coupled oscillators such as neurons, for, although neurons are not waves, the diagrams of action potentials show spikes, their activity is a function of time and their discharges show periodicity and frequency, therefore sufficiently regularly discharging neurons sharing same frequencies can synchronize their frequencies too, by the coincidence of all the spikes (or other parts of their action potentials) of their mutual action potentials, with more or less accuracy. Therefore a constant phase difference between neurons through a synchronization of their frequencies can take place and be measured as such too. When a phase difference between two given neural signals with same frequencies is constant, then their frequencies get synchronized, their discharges become homogeneous, and that has something to do with the way the signal propagates along the neural networks (“Lopes Da Silva, 2013”). Such is the case, for example, of the synchronization after a neurogenic stimulus of the muscle fibers involved in a synergic muscular contraction, necessary for the correct movement of the limbs. But, although the homogeneity developed from the synchronization of the neuronal frequencies of discharge can explain some nerve functions, like the correct movement of the limbs, it is obvious that a sufficient amount of inhomogeneity in the activity of the brain is necessary for the movement of the limbs too and, also, to represent at all a heterogeneous reality using codes; the codes have to be sufficiently heterogeneous too, even though simultaneous in different parts of the brain. For instance: A “red ball” mental object is heterogeneous, and this means, at least, round and red at the same time; this means that at least two sets of neurons coding sensory information about the round shape one of the sets and about the red colour the other set, in the cortex, when perceiving a red ball as a whole, are necessary to perceive it properly, and both neural sets discharging at the same time, but each with a different pattern of frequencies, so that the codes, “round shape” code and “red colour” code, remain different while effective at the same time. Still they will have to be discharging at the same time, simultaneously, and therefore synchronized somehow after all then, but not synchronized by their mutual frequencies, because that would imply the complete homogeneization of the codes for round shape and red colour, and the consequent impossibility to perceive the red ball with the minimum necessary heterogeneity to be recognizable. But, alas, the synchronization of frequencies is the type of neural synchronization that has been observed in laboratories, so there has to be some other hidden type of neural synchronization to be unveiled that can be able to explain the “red ball” percept (synchronization with heterogeneity). Several investigations on vision (“Zeki, S., Bartels, A., 1998, The asynchrony of consciousness, Proceedings of the Royal Society B 265, pp. 1583-85”) permitted the corroboration that, in fact, to be conscious it is mandatory to be conscious of something, and this means heterogeneity, that there cannot be consciousness outside of a mind. At the same time those investigations allowed to obtain evidence about the direct link between the neural areas coding movement (Not shape in that investigation) and colour when explaining perception, although without unveiling any mechanism of synchronization in particular. It is required another hypothetical type of neural synchronization then, other than a synchronization of frequencies. That enigmatic and hidden type of synchronization should probably and hypothetically be, without further ado, a phase syncronization between single neuronal signals of different but compatible networks in the brain cortex. Phase synchronization between single signals, between single neurons, has not been searched for in the brain cortex so far. Now, let us see what phase synchronization is and how it would be what we need to be conscious:

Phase synchronization

Given two waves, let us suppose that one of them has a regular frequency of 2 Hz (two cycles per second, two spikes per second) and 3 Hz the other one. If they maintain those two different frequencies, they will not be able to synchronize their frequencies, they will not be able to coincide by, for instance, all of their spikes, or, in the case of two neurons discharging regularly at those same two frequencies, by the spikes of their respective single action potentials of the trains of action potentials that both are simultaneously firing. For that to happen, for the synchronization of their frequencies, first of all they should keep the same frequency, both oscillating at 2 Hz or both at 3 Hz in the example. If the first wave keeps a frequency of 2 Hz and the second one a frequency of 3 Hz, although they cannot synchronize their respective frequencies and keep a constant phase difference between them this way, they can nevertheless get synchonized, and keep a constant phase difference between them anyway, in a different manner other than synchronizing their frequencies, like this: Both waves can keep a constant phase difference between them and therefore get synchronized, despite having different frequencies, if both waves coincide at the same point of the time line (abscissa), by a same phase of each wave every certain constant number of oscillations for each wave. In this example, with those frequencies, 2 Hz and 3 Hz, both waves will keep a constant phase difference between them and become synchronized this way if they coincide by a same particular phase of each wave, for instance, a spike, every two oscillations of the first wave, the one at 2 Hz, and every three oscillations of the second wave, the one at 3 Hz. Thus, through this other mechanism, a constant phase difference by a synchronization of some of their spikes, always the same spikes recurring regularly, instead of a synchronization of their frequencies (or of all of their spikes), a synchronization after all, can be established as well. Phase synchronization (synchronization of some particular regularly recurring phases) is another way to get synchronized without having to synchronize the frequencies (synchronization of all the phases). There are two ways to synchronize two waves then, two ways to establish a constant phase difference between them: First, by a synchronization of their frequencies, when both waves share the same frequency, which gives rise to a constant phase difference between all of their spikes, and, second, by a phase synchronization, a regular synchronization of some of their spikes only, when both waves do not share the same frequency, which gives rise to a constant phase difference not between all of the spikes of both waves, but between concrete, for each wave, repetitive sequences of a certain regular number of spikes of both waves, so, in a phase synchronization, instead of getting the waves mutually geared as a function of time by all of their spikes they get geared by specific and concrete regular or constant and repetitive sequences or trains of spikes, in a definite pattern. In this example, both waves, if synchronized through phase synchronization, would get geared, regularly, by a same spike of each wave every regular number of cicles, every sequence of two consecutive spikes for the first wave at 2 Hz and every train of three consecutive spikes for the second wave at 3 Hz (hence geared with a constant phase difference by spikes number 2, 4, 6, 8, 10, 12, etc., of wave 1 at 2 Hz, which would coincide in the same points in time with spikes number 3, 6, 9, 12, 15, 18, etc., of wave 2 at 3 Hz, respectively, and on that order, in seconds 1, 2, 3, 4, 5, 6, etc., of the timeline beginning in 0).

Coherent emission

A condition for a phase synchronization of two waves to be possible and take place, when they are emitted, is that the emission of both waves must be coherent, starting at the same time within a critical margin of error, because that will guarantee that both get geared as a function of time (that they coincide) by a given phase each. A coincidence of that specific definitory first phase of each wave at the same point in time will take place like that, through a coherent emission, and it will trigger the establishment of a consequent constant phase difference phenomenon between the waves, their synchronization. That will guarantee that both waves stay coupled as a function of time from then on, in phase synchronization, for a constant phase difference between them will be established from that point on in this manner, regardless of their particular frequencies (Unlike what happened in the case of a synchronization of frequencies, where sharing the same frequency was a mandatory condition). Neurons are not waves, but the coherent discharge of action potentials between neurons of different parallel networks is possible too, by the effect of the pacemakers in the nervous system. The occipital alpha rythm observed in an electroencephalogram, due to the thalamocortical loop, is a classical example of a neural pacemaker at work. Neural networks should be able to synchronize and get compatible (mutually coherent) like this, through a phase synchronization of their mutual single signals, given that neurons can act as pacemakers and a pacemaker can evoke a coherent discharge of different compatible networks it is connected with. Neural impulses travel throughout the brain, upwards, downwards and sidewards, forming interactive and feedback loops, some of them already described in detail by neuroanatomists, making pacemaking possible. Different parts of the nervous system are already acting as pacemakers of the cortex activity, like the brainstem, or the thalamus. The thalamocortical pacemaker was investigated by Bishop, and later by Llinás. More recently Edelman and Tononi named the corticocortical loop interactive activity “reentry”. Several investigations on this field have been performed through the years (for instance: “Uhlhaas, P. J. et al, 2008, The role of oscillations and synchrony in cortical networks and their putative relevance for the pathophysiology of schizophrenia, Schizophr bull 34/5, pp. 927-43”).

Diaschisis

Neurons connect with each other inside the brain, regardless of the distance between them, due to the great length the neurites can reach. This property is called diaschisis, as described by Márquez in a chapter of the “Tratado de Fisiología humana” (2000), by Tresguerres. Diaschisis implies that, when a mental task is performed, not only one area of the brain will be activated, but several. This represents the possibility for separated areas to network. This long distance but still local activity gives rise to a macroscopic functional neural networking that can be identified in the brains of mammals (See, for instance: “Mesulam MM. Large-scale neurocognitive Networks and distributed processing for attention, laguage, and memory. Ann Neurol 1990; 28: 597-613”). There is some evidence of the coupling of neural single signals, the action potentials of one single neuron, of various neurons, regardless of the distance between them (For instance, see: “Canolty RT et al. Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies. Proc Natl Acad Sci USA 2010; 107: 17356-17361”).

Mental congruence

How does the brain process mental information congruently, despite its complexity and its chaotic nature? In an A-B-C circuit, C is a correlate of A if every step of A-B-C is true (actual). True means possible in a system and verified. According to Hofstadter, as he wrote in his book, “Gödel, Escher, Bach: un eterno y grácil bucle” (2003), the congruence with external reality, the compatibility between mind and environment, depends on an appropriate interpretation of it. However, let us add that incongruence should not be considered an essential property of the brain, despite being the brain a chaotic system, for even a delusional mind can be right by chance sometimes. According to Hofstadter, a system will be coherent (congruent) with reality if every theorem is true, and theorems will be coherent (congruent) between them if they are compatible, true at the same time. A theorem is a true (verified) proposition. A neural discharge following a pattern inside a circuit or a network could be considered a theorem, once verified. Abstract thinking will be congruent with the macroscopic reality around it if the information about the environment in the brain is compatible with that environment. In a neural circuit A-B-C, A connects with B and B with C. One discharge of B will be an effect of a discharge of A transmitted to B that acts as a stimulus for B. The change of state in A will be the cause of the change of state in B, and the change of state in B will be the cause of the change of state in C. There is a cause-effect relationship between A and B, and between B and C, but not between A and C. The relationship between A and C is correlation, because the activity in C depends on the activity in A, although A is not the direct cause of the activity in C, but B (A is the correlate). Given an association and integration of neural networks through their connections, for instance, let us consider [A-B-C] neural network, which would be connected with [D-E-F] network, like this: [A-B-C]-[D-E-F]. If the [A-B-C] network stimulates the [D-E-F] network, then, and for instance, A stimulates D, B stimulates E and C stimulates F, and all of them at the same time. This means that the cause-effect relationship between networks can be understood as a congruent, or non contradictory reduction of this relationship to a cause-effect one-to-one relationship between their respective neurons, because, although networks, sets of millions of neurons, are functional units, they are made up of neurons, which are functional units on their own scale too, and still interacting with each other through their synapses, although other functional arrangements, with other apparent meanings, like a networking, can be taking place at the same time on another larger scale. This is why the abstract information processed by the brain, considered on a scale of networks, when circuits form networks, will not become contradictory despite acquiring an emergent meaning and appearance (a macroscopic mental image of a red ball will be congruent although made up by the activity of microscopic neurons). All a network needs to have a cause-effect relationship with another is A to be the cause of D, B of E an C of F at the same time A is the cause of B, B of C, D of E and E of F. This is accomplished, because microscopic neurons keep firing their action potentials on their microscopic scale regardless of the simultaneous macroscopic networking emerging from the same substrate at the same time on another bigger scale with some macroscopic meaning. In this case both phenomena, the microscopic neural activity and the macroscopic networking, would be the same phenomenon considered from two different points of view, on a microscopic scale if considered neurons, or on a macroscopic scale if considered networks. Therefore, congruence will remain on the scale of networks. The brain is a chaotic system, but order and congruence emerge in it because the fundamental piece is the same, the neuron, and also due to the dynamic polarization, and to the circuit and network structuring, also because the fundamental basic algorithm is the same, also for the said mental congruence, for the property of the memory that implies predictability and determinism within an acceptable margin of error on a macroscopic scale (We can act as if red balls truly were red balls instead of a mass of elementary particles), also due to the stability of the whole structure for enough time, for the metastability and also for the self organization, which will be discussed in chapter 4.