Head and the Nervous System Diagram

Men and women’s brains are different: men have 6.5 more gray matter in their brain than women, but women have 10 times the amount of white matter than men, according to a 2005 study from the University of California, Irvine.

The brain’s gray matter works like a computer in the body, while the white matter acts as the cables that allow the gray matter to communicate with other gray matter.   

For those looking to setting a heated debate should know this: researchers believe the differences mean the nervous system has evolved two different ways to create equal intelligence.

The nervous system consists of the brain, spinal cord, and nerves. This is the way the body communicates with the brain and vice versa.

The nervous system is divided into two key parts:

• Central nervous system (CNS): the primary control center for the body and is composed of the brain and spinal cord.
• Peripheral nervous system (PNS): of a network of nerves that connects the rest of the body to the CNS.

The two systems work together to collect information from inside and outside the body. The systems collect and process information and then dispatch instructions to the rest of the body.

The brain is the destination for information gathered by the rest of the nervous system. Once data arrives, the brain sorts and files it before sending out any necessary commands. These instructions cause the body to react, like when you instinctively pull your hand away from a hot stove.

Information conveyed through the nervous system moves along networks of cells called neurons. These neurons can only send information one way. Those transmitting to the brain are sensory neurons; those that transmit from the brain are known as motor neurons.

Although the brain is the control center, its job would not be possible without the spinal cord, which is the major conduit for information traveling between brain and body.

The brain is divided into many different sections, including the cerebrum and brain stem. These parts handle pieces of the brain’s overall workload, including storing and retrieving memory and making body movements smooth.

Peripheral system nerves branch from either the brain stem or the spinal cord. Each nerve is connected to a particular area of the torso or limbs and is responsible for communication to and from those regions.

The PNS can also be divided into smaller pieces: the somatic and autonomic systems. The somatic involves parts of the body a person can command at will, such as moving your arm, walking, and more. The autonomic helps run involuntary functions such as pumping blood, breathing, and digestion.

The nervous system can suffer from a number of afflictions, including cancer. Other problems include multiple sclerosis, in which damaged nerves prevent signals from traveling along them, or meningitis, which causes an inflammation of the membranes surrounding the brain and spinal cord.

Human Nervous System – Diagram – How It Works

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Find out about the workings of the brain and nerves.
(Image credit: Ross Toro, Livescience.com contributor)

The nervous system, essentially the body’s electrical wiring, is a complex collection of nerves and specialized cells known as neurons that transmit signals between different parts of the body. Neurons signal to other cells through fibers called axons. Chemicals called neurotransmitters are released at gaps called synapses. These communications take only a fraction of a millisecond. Sensory neurons respond to stimuli such as light and sound. Motor neurons carry activation signals to muscles and glands. Neurons are supported and fed by glial cells. “Glial” derives from the Greek word for “glue.” Vertebrates, animals with backbones and spinal columns, have central and peripheral nervous systems. The Central Nervous System is the integration and command center of the body. It consists of the brain, spinal cord and the retinas of the eyes. The Peripheral Nervous System consists of sensory neurons, ganglia (clusters of neurons) and nerves that connect the central nervous system to arms, hands, legs and feet. The Cranial Nervous System nerves connect the brain to the eyes, mouth, ears and other parts of the head. The Autonomic Nervous System nerves connect the central nervous system to the lungs, heart, stomach, intestines, bladder and sex organs. The branch of medicine that studies the nervous system is called neurology. Doctors who treat the nervous system are neurologists.

Related:

• Nervous System: Facts, Function & Diseases

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Ross Toro is a contributing infographic artist for Live Science. He specializes in explanatory graphics that deal with science topics. Ross is a former art director of the Los Angeles Times, Associated Press and United Press International. He teaches Filipino martial arts when not dabbling in cartoons and animation.

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The most detailed map of neural connections in the human brain has been created

02 June 2021
16:52

Olga Muraya

Color image of 4,000 axons transmitting nerve impulses to one neuron.

Google/Lichtman Laboratory illustration.

After long and painstaking work, researchers from Google and Harvard have published one of the most detailed 3D models of the human brain in the public domain.

The human brain has 86 billion neurons that communicate with each other through hundreds of trillions of synapses. This is an intricate network of neural connections, in the depths of which our consciousness, thoughts, feelings, memories and individuality are hidden.

The human brain is more complex than any computer that exists today. Therefore, the visualization of the full structure of all its connections looks, at first glance, a completely impossible task.

However, researchers around the world are working hard, literally picking up small areas of the human brain piece by piece. By the way, such a map of neural connections is called a “connectome”, and the science that deals with the “cartography” of the nervous system is called connectomics.

In 2020, Google researchers collaborated with colleagues at the Howard Hughes Medical Institute to create a fruit fly brain connectome. Sounds like a pretty simple task, but so far they’ve only been able to map about half of the insect’s brain.

Google and Harvard researchers recently released a similar model of the human brain. More precisely, its tiny area.

The researchers used a sample from the temporal lobe of the cerebral cortex as small as 1 mm ³ . It was dyed with special substances and cut into 5,300 layers about 30 nanometers thick. Then each of these layers was scanned using an electron microscope.

So the scientists obtained 225 million two-dimensional images, which were then “stitched” into a 3D model.

Different cells and their structures within the sample were identified using machine learning algorithms. The researchers only occasionally manually tested the accuracy with which the machines determined which cells belonged.

The end result has been named data set H01 and is one of the most complete maps of the human brain ever created. It contains information about 50,000 nerve cells, 130 million synapses, and, in addition, visualizes additional details: axons and dendrites of neurons, myelin and ciliary epithelial cells.

The most impressive thing about this dataset was that it took up a whopping 1.4 petabytes of memory. That’s over a million gigabytes.

At the same time, Google claims that this is only one millionth of a complete map of the human brain.

It turns out that not only the work of mapping this entire volume, but also the search for a place to store this incredible array of information, will present a huge challenge. In addition, researchers have yet to find a way to organize the data obtained and provide convenient access to them.

In the meantime, the currently collected H01 dataset is available for online review.

The scientific paper accompanying this achievement was published on the bioRxiv preprint site.

Recall that earlier we wrote about creating a map of the brain showing where individual words are “stored” in it (by the way, it is also available online). We also reported that scientists have found similarities between the human brain and a swarm of bees.

More news from the world of science and technology can be found in the “Science” section of the “Looking” media platform.

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• Sleep mask improves memory and concentration the next day

• An unknown brain disease was found in three children

• 319 million years: oldest vertebrate brain discovered

Is it possible to recreate a complete mouse neural network from thin layered sections of the brain? / Habr

Photo source
Dwarf multitooth, the smallest mammal by weight. Inside is a small holistic complex brain, which can already be mapped in principle

The short answer is yes, but not complete and not very accurate. That is, we still cannot copy her consciousness, but we are closer than ever. Live another twenty years – and perhaps your brain will also be backed up.

To get closer to the digitization of consciousness and such an exotic kind of immortality, it is worth first understanding living neural networks. Their reverse engineering shows us how the process of thinking (computing) can generally be arranged in well-optimized systems.

60 years ago, on September 13, 1960, scientists gathered the first symposium of biologists and engineers so that they could figure out what is the difference between a complex machine and an organism. And does it exist at all. Science was called bionics, and the goal was the application of methods of biological systems to applied engineering and new technologies. Biosystems were seen as highly efficient prototypes of new technology.

Military neuroanatomist Jack Steele was one of the people who significantly influenced the further progress in the field of technology, including in the field of AI, where such areas as neuromorphic engineering and bioinspired computing were developed. Steele was a physician, versed in psychiatry, was fond of architecture, knew how to fly an airplane and repaired his own equipment, that is, he was quite a good applied engineer. Steele’s scientific work became the prototype for the script for the film Cyborg. So with some stretch, you can call him the great-grandfather of the Terminator. And where the Terminator is, there is Skynet, as you know.

This post is based on materials from the forthcoming book by our colleague Sergei Markov “Electric Sheep Hunting: A Big Book of Artificial Intelligence”.

In general, the question of the relationship between the physiological processes occurring in the human nervous system and mental phenomena is one of the most intriguing questions of modern science. Imagine that a secret computer processor has fallen into your hands and you want to copy it. You can cut it into thin layers and meticulously copy it layer by layer. But how accurate do you need a copy to be fully or at least partially functional? Without experiments, it is extremely difficult to answer such a question.

Bionics, or biomimetics, copied the principles of biosystems or took them as a basis. Just as Leonardo da Vinci watched the flight of birds and invented the ornithopter (unfortunately, suitable materials and energy sources were not found then), so we copied increasingly complex systems in the twentieth century. RFID chips, medical adhesives (glues), hydrophobic structures, nanosensors – all this and much more was created from bioprototypes. Somewhere, the very existence of a prototype in nature made it possible to understand that the technology was possible in principle. If plants are able to synthesize sugars and starch from carbon dioxide and water, then it is possible to create a device that performs the same function.

And if evolution optimizes systems towards adaptability to the environment, then we could optimize them for our tasks. From the point of view of evolution, the human brain should consume little energy, should be resistant to physical influences (you are unlikely to like it if you completely lose memory from an apple falling on your head), a baby’s head should freely pass through the birth canal at birth, and so on.

In the case of developing a device using the same principles, we do not have these restrictions. As well as hundreds of others.

By the way, our ancestors often adhered to the “terminal” theory of thinking, assuming that processes take place somewhere remotely (in the soul) and are transmitted in the form of commands through some organ. Aristotle and his colleagues believed that the terminal of the soul is located in the heart. But the experiences of the ancient doctors were limited by the technical level of civilization. This continued until about the time Luigi Galvani discovered in 1791 that current causes muscles to contract. These experiments gave rise to research in the field of bioelectric phenomena. At some point, Cato decided to measure the potentials of everything around and began to discover animals for his measurements. He found that the outer surface of the gray matter was more positively charged than the deep structures of the brain. He also noted that the electrical currents of the brain appear to be related to the underlying function. “When I showed the monkey a raisin, but did not give it, there was a slight decrease in current” . Thanks to him, non-invasive (that is, not associated with penetration through the external barriers of the body) electroencephalography was born. In 1890, the physiologist Adolf Beck from Poland discovered low-voltage, high-frequency oscillations of electrical potentials that occur between two electrodes placed in the occipital cortex of the rabbit’s brain.

At that moment it became clear to many scientists that the brain is a fundamentally knowable thing. Perhaps this is not even a “terminal” for the divine soul, but a completely understandable electric machine, but only very complex. Or it contains such an engineering component, and it can be studied. Cato created the prerequisites for the subsequent appearance of the EEG. Modern electroencephalography was created by Berger, although he had predecessors such as Pravdich-Neminsky and others.

Two years before Cato’s experiments, in 1873, the Golgi method was discovered (named after its author, the Italian physiologist Camillo Golgi), which allows staining individual neurons (although the word “neuron” was not used until 1891).

Before the discovery of Golgi, the concept proposed by the German histologist Josef von Gerlach was popular in biology, who believed that the fibers emerging from various cell bodies are connected into a single network called the “reticulum”. The popularity of Gerlach’s ideas was due to the fact that, unlike the heart or liver, the brain and nervous system could not be divided into separate structural units: although nerve cells were described as part of tissue by many researchers of that time, the connection between nerve cells and the axons connecting them and dendrites was unclear. The main reason for this was the shortcomings of microscopy. Thanks to his discovery, Golgi saw that the branched processes of one cell body do not merge with others. He, however, did not discard Gerlach’s concept, suggesting that the long thin processes were probably connected in one continuous network.

This was similar to what the mechanics and electricians already knew. The mechanistic approach triumphed. True, it was still decidedly unclear how it works. Or at least how it might work.

Fourteen years later, in 1887, the Spanish neuroanatomist Santiago Ramón y Cajal proved that the long thin processes emerging from the cell bodies are not at all connected in a single network. The nervous system, like all other living tissues, consisted of individual elements. Ramon y Cajal and Camillo Golgi were awarded the Nobel Prize in 1906 for their work on the nervous system.0113 in Physiology or Medicine . Ramon y Cajal’s sketches, of which about 3,000 have survived to this day, remain today among the most detailed descriptions of the structural diversity of the brain and nervous system.

Sketched by Santiago Ramon y Cajal

Further research showed that we can fundamentally understand how we think – at the engineering level. And, therefore, we can do applied biomimetics.

Although it has been known since the time of Galvani that nerves can be excited electrically, the stimuli used to excite the nerves were quite difficult to control. What strength and duration should the signal be? And how can the relationship between stimulus and excitability be explained in terms of the underlying biophysics? These questions were asked at the turn of the 19th and 20th centuries by the pioneers in the study of nervous excitability Jan Horweg (Jan Leendert Hoorweg, 1841-1919, sometimes inaccurately referred to as “Goorweg”), Georges Weiss (Jules Adolphe Georges Weiss, 1851-1931) and 1007/s00422-007-0190-0″> Louis Lapicque (1866-1952) . In his first study of 1907, Lapic presents a model of the nerve, which he compares with data obtained by stimulating the nerve of a frog. This model, based on a simple capacitor circuit, will serve as the basis for future neuron cell membrane models.

Just so you understand the complexity of science in those years, it is worth giving a couple of examples. The stimulus Lapik used was a short electrical impulse delivered through two electrodes designed and manufactured specifically for this purpose. Ideally, stimulation experiments could use current pulses, but it was not easy to create suitable current sources. Instead, Lapik used a voltage source – a battery. Voltage regulation was carried out using a voltage divider, which was a long wire with a slider, similar to a modern potentiometer. It was also not easy to get accurate pulses with a duration of only a few milliseconds; a tool invented a little earlier for this was called the “rheotom”. The device consisted of a capsule lock pistol, the bullet of which first broke the first jumper, creating a current in the stimulating circuit, then tore the second jumper in its path, interrupting the contact.

The work of 1907 led Lapik to a number of theoretical considerations. He postulated that the activation of a chain of nerve cells depends on the sequential electrical stimulation of each cell by the impulse or action potential of the previous one. Lapik proposed a theory of nervous processes that resembled an adjustment or resonance between oscillatory radio circuits.

In 1943, Lapik’s book La machine nerveuse [The Nerve Machine] was published, summing up the scientist’s many years of research.

Paris publishing house: Maison parisienne Neurdein (ND. Phot.), s.d.

Often in reasoning about the results of Lapik’s work for computational neuroscience & Gomila, T. (2008). Handbook of Cognitive Science: An Embodied Approach. Elsevier Science. // https://books.google.ru/books?id=jxnhqHuo3gQC”> one may come across the statement that Lapik is the creator and researcher of the first model of the neuron, called “integrate-and-work” [integrate-and- fire]. In accordance with this model, the algorithm of the neuron operation can be described as follows: when a current is applied to the input of the neuron, the potential difference (voltage) on the membrane increases with time until it reaches a certain threshold value, at which there is an abrupt change in the potential at the output, the voltage is reset until the residual potential, after which the process can be repeated again and again. In fact, the connection between nerve excitation and the formation of a nerve impulse was still unclear at the time of Lapik, and the scientist does not hypothesize either about this, or about how the membrane returns to its original state after the issuance of an impulse.

Further development of Lapic’s ideas within the framework of computational neuroscience led to the emergence of many more accurate and complete models of the biological neuron. These include leaky integrate-and-fire models, fractional-order leaky integrate-and-fire models, Galvez-Löckerbach model [ Galves-Locherbach model], “exponential integrate-and-fire” model, and many others. Nobel Prize 1963 years old was issued for the research of Sir Alan Lloyd Hodgkin (Sir Alan Lloyd Hodgkin, 1914-1998) and Sir Andrew Fielding Huxley (Sir Andrew Fielding Huxley, 1917-2012, do not confuse with the writer).

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Longfin coastal squid (Doryteuthis pealeii), like other squids, is an extremely convenient model organism for neurophysiologists due to the presence of giant axons in it. The squid giant axon is a very large (usually about 0.5 mm in diameter, but sometimes as large as 1.5 mm) axon that controls part of the squid’s water jet system, used primarily for short but very fast movements in the water. Between the tentacles of the squid there is a siphon through which water can be quickly pushed out due to contractions of the muscles of the body wall of the animal. This contraction is initiated by action potentials in the giant axon. Because electrical resistance is inversely proportional to the object’s cross-sectional area, action potentials propagate faster in a larger axon than in a smaller one. Therefore, an increase in the diameter of the giant axon was maintained in the process of evolution, as it allowed an increase in the speed of the muscle reaction. This was a real gift for Hodgkin and Huxley, who were interested in the ionic mechanism of action potentials – after all, due to the large diameter of the axon, it was possible to install clamping electrodes in its lumen!

Source

The Hodgkin-Huxley model is a system of non-linear differential equations that approximately describes the electrical characteristics of excited cells. The result was a model that served as the basis for more detailed research – this was the main breakthrough in neurophysiology of the twentieth century.

One of the most interesting projects is carried out by scientists from the laboratory of Sebastian Seung. The immediate goal of the project was to create a map of connections between neurons in the retina of a mouse named Harold. The retina was chosen as a model object for testing the technologies necessary to achieve a long-term scientific goal – a complete description of the human brain connectome. The mouse brain was removed from the skull and sliced ​​into thin layers.

The resulting sections were passed through an electron microscope. When the laboratory staff realized that it would take about fifty hours of specialist time to map the connections of a single neuron, and mapping the mouse retina from a group of one hundred scientists would take almost two hundred years, it became clear that a fundamentally different solution was needed. And it was found. It was the creation of the online game EyeWire, in which players compete with each other in coloring photographs of sections of a mouse brain.

In 2014, two years after the launch of EyeWire, the laboratory staff made the first discovery and reported it in the journal Nature. Scientists were able to figure out exactly how mammals recognize movement. When light hits the photoreceptor cells, they transmit a signal to the bipolar cells, then to the amacrine cells, and finally to the ganglion cells. The scientists analyzed 80 stellate amacrine neurons (29 of which EyeWire players helped describe) and the bipolar cells connected to them. They noticed that different types of bipolar cells connect to amacrine neurons in different ways: bipolar cells of one type are located far from the “soma” (body) of the stellate cell and transmit a signal quickly, cells of another type are located close, but the signal is transmitted with a delay.

If the stimulus in the field of view moves away from the body (soma) of the stellate amacrine cell, then the “slow” bipolar cell is activated first, then the “fast” one. Then, despite the delay, the signals of both types of cells reach the stellate amacrine neuron at the same time, it emits a strong signal and passes it on to the ganglion cells. If the stimulus moves towards the soma, the signals of different types of bipolar neurons do not “meet” and the signal of the amacrine cell is weak .

Player-labeled data was used to train appropriate machine learning models on it, which could then color themselves . It’s kind of ironic that these models are based on convolutional neural networks (we’ll talk about them in more detail a little later), created, in turn, under the influence of scientific data obtained during the study of the visual cortex of the brain.

On April 2, 2013, the BRAIN Initiative began. The first building block was an article by Paul Alivisatos, which outlined experimental plans for a more modest project, including methods that could be used to build a “functional connectome” and technologies that would need to be developed during the project. From worms and flies, it was planned to move on to larger biosystems, in particular, the pygmy millident. It is the smallest mammal known to science in terms of body mass, and its brain consists of only about a million neurons. It will be possible to move from shrews to primates, including, at the last stage, to humans.

The first connectome of a living creature, namely the nematode C. elegans, was built back in 1986 by a group of researchers led by biologist Sydney Brenner (Sydney Brenner, 1927-2019) from Cambridge. Brenner and his colleagues carefully sliced ​​millimeter-sized worms into thin slices and photographed each slice using a film camera mounted on an electron microscope, and then manually traced 1986.0056″> all connections between neurons from the obtained images. However, C. elegans has only 302 neurons and about 7,600 synapses. In 2016, a team of scientists from Delhousie University in Canada repeated the feat of their colleagues for the larvae of the marine tunicate Ciona intestinalis, whose central nervous system, as it turned out, consisted of 177 neurons and 6,618 synaptic connections . However, it should be noted that the methods used to construct the connectome are ineffective for large nervous systems. Researchers did not seriously consider embarking on much larger projects until 2004, when physicist Winfried Denk and neuroanatomist Heinz Horstmann proposed using an automated microscope to cut and visualize the brain, as well as software to collect and join the 90,193 resulting images .

In 2019, Dr. Scott Emmons published a publication in Nature with a detailed account of recreating the connectome of the nematode Caenorhabditis elegans using the new method . A year earlier, a team of scientists led by Zhihao Zheng of Princeton University completed a scan of the Drosophila brain, which consists of about 100,000 neurons. The system developed by Zheng and his colleagues made it possible to pass over 7,000 very thin sections of the brain of a fly through a transmission scanning electron microscope, each of which was about 40 nm thick, and the total size of the resulting images was S., Perlman E., Robinson C. G., Nichols M., Milkie D., Torrens O., Price J., Fisher C. B., Sharifi N., Calle-Schuler S. A., Kmecova L., Ali I. J., Karsh B., Trautman E. T., Bogovic J. A., Hanslovsky P., Jefferis G. S. X. E., Kazhdan M., Khairy K., Saalfeld S., Fetter R. D., Bock D. D. A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster / Cell, Vol. 174, Iss. 3, p. 730-743.E22, July 26, 2018 // https://doi.org/10.1016/j.cell.2018.06.019″> amounted to 40 trillion pixels .

In April 2019, the staff of the Institute of the Brain. Allen in Seattle celebrated the latest milestone in a project to map the one cubic millimeter mouse brain with its 100,000 neurons and one billion connections between them. To process a sample the size of a mustard seed, microscopes ran continuously for five months, collecting more than 100 million images of 25,000 sections of the visual cortex. It then took about three months for software developed by the institute’s scientists to combine the images into a single 2 petabyte 3D array. All the images of our planet collected over more than 30 years by Landsat missions occupy only about 1.3 petabytes, which makes mouse brain scans practically “a whole world in a grain of sand.” The ultimate goal – a nanoscale human brain connectome – is still a long way off. The number of neurons in it is comparable to the number of stars in the Milky Way (about 10 ¹¹ ). With today’s imaging technology, it will take dozens of microscopes running around the clock for a thousand years to collect the data needed to achieve the ultimate goal. But advances in microscopy, as well as the development of more powerful computers and algorithms for image analysis, have advanced the field of connectomics so rapidly that it surprises researchers themselves. “Five years ago, it was too ambitious to think in terms of a cubic millimeter,” says Reid. Today, many researchers believe that complete mapping of the mouse brain, which is about 500 cubic millimeters, will be possible in the next decade. “Today, mapping the human brain at the synaptic level may seem incredible. But if progress continues at the same pace, both in computing power and in scientific methods, another thousandfold increase in capabilities is already does not strike us as unthinkable .”

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The BRAIN Initiative is not the only major program in this area. Scientists from the Blue Brain Project and the Human Brain Project are also busy creating a functional model of the rat brain (with an eye on the human brain). The China Brain Project does not stand still either.

Actually, now that you understand the complexity of these biological prototypes, you can move on to the engineering approach and gradually begin to discuss the application of the principles in modern computing.