Neurosciences - distance learning. Young scientists: neuroscientist Anatoly Buchin about squids, brain modeling and the daily benefits of neuroscience Where do they study to become a neuroscientist?

Ecology of consciousness: Life. It has been absolutely proven that our brain is a wildly plastic thing, and individual training seriously affects it - to a much greater extent than innate predispositions.

When compared with the young of other animals, we can say that a person is born with an underdeveloped brain: its mass in a newborn is only 30% of the mass of the adult brain. Evolutionary biologists suggest that we must be born premature in order for our brains to develop by interacting with the environment. Scientific journalist Asya Kazantseva in the lecture “Why should the brain learn?” within the framework of the “Art Education 17/18” program she spoke

About the learning process from the point of view of neurobiology

and explained how the brain changes under the influence of experience, as well as how sleep and laziness are useful during study.

Who studies the phenomenon of learning

The question of why the brain needs to learn is addressed by at least two important sciences - neurobiology and experimental psychology. Neurobiology, which studies the nervous system and what happens in the brain at the level of neurons at the time of learning, most often works not with people, but with rats, snails and worms. Experimental psychologists try to understand what things influence a person's learning ability: for example, they give him an important task that tests his memory or learning ability and see how he copes with it. These sciences have developed intensively in recent years.

If we look at learning from the point of view of experimental psychology, it is useful to remember that this science is the heir of behaviorism, and behaviorists believed that the brain is a black box, and they were fundamentally not interested in what happens in it. They perceived the brain as a system that can be influenced by stimuli, after which some kind of magic happens in it, and it reacts in a certain way to these stimuli. Behaviorists were interested in what this reaction might look like and what could influence it. They believed thatlearning is a change in behavior as a result of mastering new information

This definition is still widely used in cognitive science. Let's say, if a student was given Kant to read and he remembered that there is “a starry sky above his head and a moral law within me,” he voiced this on the exam and was given an “A”, then learning has occurred.

On the other hand, the same definition applies to the behavior of the sea hare (Aplysia). Neuroscientists often conduct experiments with this mollusk. If you shock an Aplysia in its tail, it begins to fear the surrounding reality and retract its gills in response to weak stimuli that it was not afraid of before. Thus, she also experiences a change in behavior and learning. This definition can be applied to even simpler biological systems. Let's imagine a system of two neurons connected by one contact. If we apply two weak current pulses to it, then its conductivity will temporarily change and it will become easier for one neuron to send signals to another. This is also learning at the level of this small biological system. Thus, from the learning that we observe in external reality, we can build a bridge to what happens in the brain. It contains neurons, changes in which affect our response to the environment, i.e., the learning that has occurred.

How the brain works

But to talk about the brain, you need to have a basic understanding of how it works. After all, each of us has these one and a half kilograms of nervous tissue in our heads. The brain is made up of 86 billion nerve cells, or neurons. A typical neuron has a cell body with many processes. Some of the processes are dendrites, which collect information and transmit it to the neuron. And one long process, the axon, transmits it to the next cells. The transfer of information within one nerve cell means an electrical impulse that travels along the process, as if through a wire. One neuron communicates with another through a point of contact called a “synapse”, the signal travels through chemicals. The electrical impulse leads to the release of neurotransmitter molecules: serotonin, dopamine, endorphins. They leak through the synaptic cleft, affect the receptors of the next neuron, and it changes its functional state - for example, channels open on its membrane through which ions of sodium, chlorine, calcium, potassium, etc. begin to pass. This leads to that, in turn, a potential difference is also formed on it, and the electrical signal goes further, to the next cell.

But when a cell transmits a signal to another cell, this is most often not enough for any noticeable changes in behavior, because one signal can also occur by chance due to some disturbances in the system. To exchange information, cells transmit many signals to each other. The main coding parameter in the brain is the frequency of impulses: when one cell wants to transmit something to another cell, it begins to send hundreds of signals per second. By the way, early research mechanisms from the 1960s and 70s generated an audio signal. An electrode was implanted into the brain of an experimental animal, and by the speed of the machine gun noise that was heard in the laboratory, one could understand how active the neuron was.

The pulse frequency coding system operates at different levels of information transmission - even at the level of simple visual signals. We have cones on our retina that respond to different wavelengths: short (in the school textbook they are called blue), medium (green) and long (red). When a certain wavelength of light enters the retina, different cones are excited to different degrees. And if the wave is long, then the red cone begins to intensively send a signal to the brain so that you understand that the color is red. However, everything is not so simple here: the spectrum of sensitivity of the cones overlaps, and the green one also pretends that it saw something like that. Then the brain analyzes this on its own.

How the brain makes decisions

Principles similar to those used in modern mechanical research and experiments on animals with implanted electrodes can be applied to much more complex behavioral acts. For example, in the brain there is a so-called pleasure center - the nucleus accumbens. The more active this area is, the more the subject likes what he sees, and the higher the likelihood that he will want to buy it or, for example, eat it. Experiments with a tomograph show that based on a certain activity of the nucleus accumbens, it is possible to tell even before a person voices his decision, say, regarding the purchase of a blouse, whether he will buy it or not. As the excellent neuroscientist Vasily Klyucharyov says, we do everything to please our neurons in the nucleus accumbens.

The difficulty is that in our brain there is no unity of judgment; each department can have its own opinion about what is happening. The story similar to cone spores in the retina repeats itself with more complex things. Let's say you saw a blouse, you liked it, and your nucleus accumbens emits signals. On the other hand, this blouse costs 9 thousand rubles, and the salary is still a week away - and then your amygdala, or amygdala (the center associated primarily with negative emotions), begins to emit its electrical impulses: “Listen, there is not enough money left. If we buy this blouse now, we will have problems.” The frontal cortex makes a decision depending on who yells louder - the nucleus accumbens or the amygdala. And here it is also important that each time subsequently we are able to analyze the consequences to which this decision led. The fact is that the frontal cortex communicates with the amygdala, the nucleus accumbens, and the parts of the brain associated with memory: they tell it what happened after the last time we made such a decision. Depending on this, the frontal cortex may pay more attention to what the amygdala and nucleus accumbens are telling it. This is how the brain can change under the influence of experience.

Why are we born with small brains?

All human children are born underdeveloped, literally premature compared to the young of any other species. No animal has such a long childhood as humans, and they have no offspring that are born with such a small brain relative to the mass of the adult brain: in a human newborn it is only 30%.

All researchers agree that we are forced to give birth to humans immature due to the impressive size of their brains. The classic explanation is the obstetric dilemma, that is, the story of the conflict between upright posture and a large head. To give birth to a baby with such a head and large brain, you need to have wide hips, but it is impossible to widen them indefinitely, because this will interfere with walking. According to anthropologist Holly Dunsworth, in order to give birth to more mature children, it would be enough to increase the width of the birth canal by only three centimeters, but evolution still stopped the expansion of the hips at some point. Evolutionary biologists have suggested that perhaps we should be born premature in order for our brains to develop in interaction with the external environment, since the womb as a whole is quite sparse in stimuli.

There is a famous study by Blackmore and Cooper. In the 70s, they conducted experiments with kittens: they kept them in the dark most of the time and put them in a lighted cylinder for five hours a day, where they received an unusual picture of the world. One group of kittens saw only horizontal stripes for several months, while another group saw only vertical stripes. As a result, the kittens had big problems with the perception of reality. Some crashed into the legs of chairs because they did not see vertical lines, others ignored horizontal ones in the same way - for example, they did not understand that the table had an edge. They were tested and played with a stick. If a kitten grew up among horizontal lines, then it sees and catches the horizontal stick, but simply does not notice the vertical one. Then they implanted electrodes into the kittens’ cerebral cortex and looked at how the stick should be tilted so that the neurons began to emit signals. It is important that nothing would happen to an adult cat during such an experiment, but the world of a small kitten, whose brain is just learning to perceive information, may be forever distorted as a result of such an experiment. Neurons that have never been affected stop functioning.

We are accustomed to thinking that the more connections there are between different neurons and parts of the human brain, the better. This is true, but with certain reservations. It is necessary not only that there be a lot of connections, but that they have some relation to real life. A one and a half year old child has much more synapses, that is, contacts between neurons in the brain, than a Harvard or Oxford professor. The problem is that these neurons are connected chaotically. At an early age, the brain matures rapidly and its cells form tens of thousands of synapses between everything and everyone. Each neuron spreads its processes in all directions, and they cling to everything they can reach. But then the “use it or lose it” principle comes into play. The brain lives in the environment and tries to cope with different tasks: the child is taught to coordinate movements, grab a rattle, etc. When he is shown how to eat with a spoon, connections remain in his cortex that are useful for eating with a spoon, since it is through them he drove nervous impulses. And the connections that are responsible for throwing the mess all over the room become less pronounced because parents do not encourage such actions.

The processes of synapse growth are quite well studied at the molecular level. Eric Kandel was given the Nobel Prize for his idea of studying memory in non-human subjects. A person has 86 billion neurons, and until a scientist understood these neurons, he would have to exhaust hundreds of subjects. And since no one allows so many people to open their brains to see how they learned to hold a spoon, Kandel came up with the idea of working with snails. Aplysia is a super convenient system: you can work with it by studying just four neurons. In fact, this mollusk has more neurons, but its example makes it much easier to identify systems associated with learning and memory. During the experiments, Kandel realized that short-term memory is a temporary increase in the conductivity of existing synapses, and long-term memory consists of the growth of new synaptic connections.

This turned out to be applicable to humans as well - it's like we walk on grass. At first we don't care where we go to the field, but gradually we make a path, which then turns into a dirt road, and then into an asphalt street and a three-lane highway with streetlights. In a similar way, nerve impulses make their own paths in the brain.

How associations are formed

Our brain is designed this way: it forms connections between events that occur simultaneously. Typically, during the transmission of a nerve impulse, neurotransmitters are released that act on the receptor, and the electrical impulse goes to the next neuron. But there is one receptor that doesn't work that way, it's called NMDA. This is one of the key receptors for memory formation at the molecular level. Its peculiarity is that it works if the signal comes from both sides at the same time.

All neurons lead somewhere. One may lead to a large neural network that is connected to the sound of a trendy song in a cafe. And others - to another network related to the fact that you went on a date. The brain is designed to connect cause and effect; at the anatomical level, it is able to remember that there is a connection between a song and a date. The receptor is activated and allows calcium to pass through. It begins to enter into a huge number of molecular cascades that lead to the operation of some previously inactive genes. These genes carry out the synthesis of new proteins, and another synapse grows. This way, the connection between the neural network responsible for the song and the network responsible for the date becomes stronger. Now even a weak signal is enough to send a nerve impulse and form an association.

How learning affects the brain

There is a famous story about London taxi drivers. I don’t know how it is now, but literally a few years ago, in order to become a real taxi driver in London, you had to pass an orientation exam in the city without a navigator - that is, know at least two and a half thousand streets, one-way traffic, road signs, prohibitions to a stop, and also be able to build the optimal route. Therefore, to become a London taxi driver, people took courses for several months. The researchers recruited three groups of people. One group is those enrolled in courses to become taxi drivers. The second group are those who also attended courses, but dropped out. And people from the third group did not even think about becoming taxi drivers. The scientists gave all three groups CT scans to look at the density of gray matter in the hippocampus. This is an important area of the brain associated with memory formation and spatial thinking. It was found that if a person did not want to become a taxi driver or wanted to, but did not, then the density of gray matter in his hippocampus remained the same. But if he wanted to become a taxi driver, completed training and really mastered a new profession, then the density of gray matter increased by a third - that’s a lot.

And although it is not entirely clear where the cause is and where the effect is (either people really mastered a new skill, or this area of the brain was initially well developed for them and therefore it was easy for them to learn), our brain is definitely a wildly plastic thing, and individual training seriously influences it - to a much greater extent than innate predispositions. It is important that even at 60 years of age, learning affects the brain. Of course, not as efficiently and quickly as at 20, but in general the brain retains some ability for plasticity throughout life.

Why should the brain be lazy and sleep?

When the brain learns something, it grows new connections between neurons. And this process is slow and expensive; it requires spending a lot of calories, sugar, oxygen, and energy. In general, the human brain, despite the fact that its weight is only 2% of the weight of the entire body, consumes about 20% of all the energy we receive. Therefore, whenever possible, he tries not to learn anything, not to waste energy. It's actually very nice of him, because if we memorized everything we see every day, we'd go crazy pretty quickly.

In learning, from the point of view of the brain, there are two fundamentally important points. The first is that, when we master any skill, it becomes easier for us to do things right than wrong. For example, you learn to drive a car with a manual transmission, and at first you don't care whether you shift from first to second or from first to fourth. For your hand and brain, all these movements are equally likely; It doesn’t matter to you which way to send your nerve impulses. And when you are already a more experienced driver, it is physically easier for you to change gears correctly. If you get into a car with a fundamentally different design, you will again have to think and control with an effort of will so that the impulse does not go along the beaten path.

Second important point:

the main thing in learning is sleep

It has many functions: maintaining health, immunity, metabolism and various aspects of brain function. But all neuroscientists agree that The most important function of sleep is working with information and learning. When we have mastered a skill, we want to form long-term memory. New synapses take several hours to grow; this is a long process, and it is most convenient for the brain to do this precisely when you are not busy with anything. During sleep, the brain processes information received during the day and erases what should be forgotten from it.

There is an experiment with rats where they were taught to walk through a maze with electrodes implanted in their brains and they found that in their sleep they repeated their path through the maze, and the next day they walked along it better. Many human tests have shown that what we learn before going to bed is remembered better than what we learn in the morning. It turns out that students who start preparing for the exam somewhere closer to midnight are doing everything right. For the same reason, it is important to think about problems before going to bed. Of course, it will be more difficult to fall asleep, but we will download the question into the brain, and maybe in the morning some solution will come. By the way, dreams are most likely just a side effect of information processing.

How learning depends on emotions

Learning is highly dependent on attention, because it aims to send impulses over and over again along specific paths of the neural network. From a huge amount of information, we focus on something and take it into working memory. Then what we focus on ends up in long-term memory. You may have understood my entire lecture, but that doesn't mean it will be easy for you to retell it. And if you draw a bicycle on a piece of paper right now, this does not mean that it will ride well. People tend to forget important details, especially if they are not bike experts.

Children have always had problems with attention. But now in this sense everything is becoming simpler. In modern society, specific factual knowledge is no longer so needed - there is simply an incredible amount of it. Much more important is the ability to quickly navigate information and distinguish reliable sources from unreliable ones. We almost no longer need to concentrate on the same thing for a long time and remember large amounts of information - It's more important to switch quickly. In addition, more and more professions are now appearing just for people who find it more difficult to concentrate.

There is another important factor influencing learning - emotions. In fact, this is generally the main thing that we have had over many millions of years of evolution, even before we grew all this huge frontal cortex. We evaluate the value of mastering a particular skill from the point of view of whether it makes us happy or not. Therefore, it’s great if we manage to involve our basic biological emotional mechanisms in learning. For example, building a motivation system in which the frontal cortex does not think that we must learn something through perseverance and determination, but in which the nucleus accumbens says that it just damn enjoys this activity.

Anatoly Buchin

Where he studied: Faculty of Physics and Mechanics of the Polytechnic University, Ecole Normale Supérieure in Paris. Currently a postdoc at the University of Washington.

What he studies: computational neuroscience

Special features: plays the saxophone and flute, does yoga, travels a lot

My interest in science arose in childhood: I was fascinated by insects, collected them, studied their lifestyle and biology. Mom noticed this and brought me to the Laboratory of Ecology of Marine Benthos (LEMB) (benthos is a collection of organisms living on the ground and in the soil of the bottom of reservoirs. - Note ed.) at the St. Petersburg City Palace of Youth Creativity. Every summer, from 6th to 11th grade, we went on expeditions to the White Sea in the Kandalaksha Nature Reserve to observe invertebrate animals and measure their numbers. At the same time, I participated in biological Olympiads for schoolchildren and presented the results of my work on expeditions as scientific research. In high school, I became interested in programming, but doing it exclusively was not very interesting. I was good at physics, and I decided to find a specialization that would combine physics and biology. That's how I ended up at Polytechnic.

The first time I came to France after my undergraduate degree was when I won a scholarship to study for a master’s program at René Descartes University in Paris. I interned extensively in laboratories and learned to record neuronal activity in brain slices and analyze the responses of nerve cells in a cat's visual cortex during the presentation of a visual stimulus. After receiving my master's degree, I returned to St. Petersburg to complete my studies at the Polytechnic University. In the last year of my master's degree, my supervisor and I prepared a Russian-French project for writing a dissertation, and I won funding by taking part in the École Normale Supérieure competition. For the last four years I have worked under dual scientific supervision - Boris Gutkin in Paris and Anton Chizhov in St. Petersburg. Shortly before finishing my dissertation, I went to a conference in Chicago and learned about a postdoc position at the University of Washington. After the interview, I decided to work here for the next two or three years: I liked the project, and my new supervisor, Adrienne Fairhall, and I had similar scientific interests.

About computational neuroscience

The object of study of computational neurobiology is the nervous system, as well as its most interesting part - the brain. To explain what mathematical modeling has to do with it, we need to talk a little about the history of this young science. In the late 80s, the journal Science published an article in which they first started talking about computational neurobiology, a new interdisciplinary field of neuroscience that deals with the description of information and dynamic processes in the nervous system.

In many ways, the foundation of this science was laid by biophysicist Alan Hodgkin and neurophysiologist Andrew Huxley (brother of Aldous Huxley. - Note ed.). They studied the mechanisms of generation and transmission of nerve impulses in neurons, choosing squid as a model organism. At that time, microscopes and electrodes were far from modern ones, and squids had such thick axons (the processes through which nerve impulses travel) that they were visible even to the naked eye. This has helped squid axons become a useful experimental model. The discovery of Hodgkin and Huxley was that they explained, using experiment and a mathematical model, that the generation of a nerve impulse is carried out by changing the concentration of sodium and potassium ions passing through the membranes of neurons. Subsequently, it turned out that this mechanism is universal for neurons of many animals, including humans. It sounds unusual, but by studying squid, scientists were able to learn how neurons transmit information in humans. Hodgkin and Huxley received the Nobel Prize for their discovery in 1963.

The task of computational neurobiology is to systematize a huge amount of biological data about information and dynamic processes occurring in the nervous system. With the development of new methods for recording neural activity, the amount of data on brain function is growing every day. The volume of the book “Principles of Neural Science” by Nobel laureate Eric Kandel, which sets out basic information about the work of the brain, increases with each new edition: the book began with 470 pages, and now its size is more than 1,700 pages. In order to systematize such a huge set of facts, theories are needed.

About epilepsy

About 1% of the world's population suffers from epilepsy - that's 50–60 million people. One of the radical treatment methods is to remove the area of the brain where the attack originates. But it's not that simple. About half of epilepsy in adults occurs in the temporal lobe of the brain, which is connected to the hippocampus. This structure is responsible for the formation of new memories. If a person's two hippocampi are cut out on either side of their brain, they will lose the ability to remember new things. It will be like a continuous Groundhog Day, since a person will only be able to remember something for 10 minutes. The essence of my research was to predict less radical, but other possible and effective ways to combat epilepsy. In my dissertation, I tried to understand how an epileptic seizure begins.

To understand what happens to the brain during an attack, imagine that you came to a concert and at some point the hall exploded with applause. You clap at your own rhythm, and the people around you clap at a different rhythm. If enough people start clapping the same way, you will find it difficult to keep up your rhythm and will likely end up clapping along with everyone else. Epilepsy works in a similar way when neurons in the brain begin to become highly synchronized, that is, generate impulses at the same time. This synchronization process can involve entire areas of the brain, including those that control movement, causing a seizure. Although most seizures are characterized by the absence of seizures, because epilepsy does not always occur in the motor areas.

Let's say two neurons are connected by excitatory connections in both directions. One neuron sends an impulse to another, which excites it, and it sends the impulse back. If the excitatory connections are too strong, this will lead to an increase in activity due to the exchange of impulses. Normally, this does not happen, since there are inhibitory neurons that reduce the activity of overly active cells. But if inhibition stops working properly, it can lead to epilepsy. This is often due to excessive accumulation of chlorine in neurons. In my work, I developed a mathematical model of a network of neurons that can go into epilepsy mode due to the pathology of inhibition associated with the accumulation of chlorine inside neurons. In this I was helped by recordings of the activity of neurons in human tissue obtained after operations on epileptic patients. The constructed model allows us to test hypotheses regarding the mechanisms of epilepsy in order to clarify the details of this pathology. It turned out that restoring the balance of chlorine in pyramidal neurons can help stop an epileptic attack by restoring the balance of excitation - inhibition in the network of neurons. My second supervisor, Anton Chizhov at the Physico-Technical Institute in St. Petersburg, recently received a grant from the Russian Science Foundation for the study of epilepsy, so this line of research will continue in Russia.

Today there is a lot of interesting work in the field of computational neuroscience. For example, in Switzerland there is a Blue Brain Project, the goal of which is to describe in as much detail as possible a small part of the brain - the somatosensory cortex of the rat, which is responsible for performing movements. Even in the small brain of a rat there are billions of neurons, and they are all connected to each other in a certain way. For example, in the cortex, one pyramidal neuron forms connections with approximately 10,000 other neurons. The Blue Brain Project recorded the activity of about 14,000 nerve cells, characterized their shape, and reconstructed about 8,000,000 connections between them. Then, using special algorithms, they connected the neurons together in a biologically plausible way so that activity could appear in such a network. The model confirmed the theoretically found principles of cortical organization - for example, the balance between excitation and inhibition. And now in Europe there is a big project called the Human Brain Project. It must describe the entire human brain, taking into account all the data that is available today. This international project is a kind of Large Hadron Collider from neuroscience, since about a hundred laboratories from more than 20 countries participate in it.

Critics of the Blue Brain Project and the Human Brain Project have questioned how important the sheer amount of detail is to describe how the brain works. For comparison, how important is the description of Nevsky Prospekt in St. Petersburg on a map where only continents are visible? However, trying to pull together a huge amount of data is certainly important. In the worst case, even if we do not fully understand how the brain works, having built such a model, we can use it in medicine. For example, to study the mechanisms of various diseases and model the action of new drugs.

In the USA, my project is devoted to studying the nervous system of Hydra. Despite the fact that even in school biology textbooks it is one of the first studied, its nervous system is still poorly understood. Hydra is a relative of the jellyfish, so it is just as transparent and has a relatively small number of neurons - from 2 to 5 thousand. Therefore, it is possible to simultaneously record activity from virtually all cells of the nervous system. For this purpose, a tool such as “calcium imaging” is used. The fact is that every time a neuron discharges, its calcium concentration inside the cell changes. If we add a special paint that begins to glow when the calcium concentration increases, then each time a nerve impulse is generated we will see a characteristic glow, by which we can determine the activity of the neuron. This allows activity to be recorded in a living animal during behavior. Analysis of such activity will allow us to understand how the hydra's nervous system controls its movement. Analogies obtained from such research can be used to describe the movement of more complex animals, such as mammals. And in the long term - in neuroengineering to create new systems for controlling nervous activity.

On the importance of neuroscience for society

Why is neuroscience so important to modern society? Firstly, it is an opportunity to develop new treatments for neurological diseases. How can you find a cure if you don't understand how it works at the level of the whole brain? My supervisor in Paris, Boris Gutkin, who also works at the Higher School of Economics in Moscow, studies cocaine and alcohol addiction. His work is devoted to describing those changes in the reinforcement system that lead to addiction. Secondly, these are new technologies - in particular, neuroprosthetics. For example, a person who was left without an arm, thanks to an implant implanted in the brain, will be able to control artificial limbs. Alexey Osadchiy at HSE is actively involved in this area in Russia. Thirdly, in the long term, this is an entry into IT, namely machine learning technology. Fourthly, this is the sphere of education. Why, for example, do we believe that 45 minutes is the most effective lesson length in school? This issue may be worth exploring better using insights from cognitive neuroscience. This way we can better understand how we can teach more effectively in schools and universities and how to plan our working day more effectively.

About networking in science

In science, the issue of communication between scientists is very important. Networking requires participation in scientific schools and conferences to keep abreast of the current state of affairs. Scientific school is such a big party: for a month you find yourself among other PhD students and postdocs. During your studies, famous scientists come to you and talk about their work. At the same time, you are working on an individual project, and you are being supervised by someone more experienced. It is equally important to maintain a good relationship with your manager. If a master's student does not have good letters of recommendation, he is unlikely to be accepted for an internship. The internship determines whether he will be hired to write his dissertation. From the results of the dissertation - further scientific life. At each of these stages, they always ask for feedback from the manager, and if a person did not work very well, this will become known quite quickly, so it is important to value your reputation.

In terms of long-term plans, I plan to do several postdocs before finding a permanent position at a university or research laboratory. This requires a sufficient number of publications, which are currently in progress. If everything goes well, I have thoughts of returning to Russia in a few years to organize my own laboratory or scientific group here.

Anatoly Buchin

Where he studied: Faculty of Physics and Mechanics of the Polytechnic University, Ecole Normale Supérieure in Paris. Currently a postdoc at the University of Washington.

What he studies: computational neuroscience

Special features: plays the saxophone and flute, does yoga, travels a lot

About computational neuroscience

About epilepsy

On the importance of neuroscience for society

About networking in science

Neurobiology studies the nervous system of humans and animals, considering issues of structure, functioning, development, physiology, pathology of the nervous system and brain. Neurobiology is a very broad scientific field, covering many areas, for example, neurophysiology, neurochemistry, neurogenetics. Neurobiology is closely related to cognitive sciences, psychology, and is increasingly influential in the study of socio-psychological phenomena.

The study of the nervous system in general and the brain in particular can take place at the molecular or cellular level, when the structure and functioning of individual neurons is studied, at the level of individual clusters of neurons, as well as at the level of individual systems (cerebral cortex, hypothalamus, etc.) and the entire nervous system as a whole, including the brain, the spinal cord, and the entire network of neurons in the human body.

Neuroscientists can solve completely different problems and answer, sometimes, the most unexpected questions. How to restore brain function after a stroke and which cells in human brain tissue influenced its evolution - all these questions are within the competence of neuroscientists. And also: why coffee invigorates, why we see dreams and whether they can be controlled, how genes determine our character and mental structure, how the functioning of the human nervous system affects the perception of tastes and smells, and many, many others.

One of the promising areas of research in neurobiology today is the study of the connection between consciousness and action, that is, how the thought of performing an action leads to its completion. These developments are the basis for the creation of fundamentally new technologies, which we currently have no idea about, or those that are beginning to develop rapidly. An example of this is the creation of sensitive limb prostheses that can completely restore the functionality of a lost limb.

According to experts, in addition to solving “serious” problems, the developments of neuroscientists can soon be used for entertainment purposes, for example, in the computer games industry to make them even more realistic for the player, in the creation of special sports exoskeletons, as well as in the military industry.

The topics for study in neurobiology, despite a lot of research in this area and increased interest from the scientific community, are not getting smaller. Therefore, several more generations of scientists will have to solve the mysteries that lie within the human brain and nervous system.

A neuroscientist is a scientist who works in one of the fields of neuroscience. He can engage in fundamental science, that is, conduct research, observations and experiments, forming new theoretical approaches, finding new general patterns that can explain the origin of particular cases. In this case, the scientist is interested in general questions about the structure of the brain, the characteristics of the interaction of neurons, studies the causes of neurological diseases, etc.

On the other hand, a scientist can devote himself to practice, deciding how to apply known fundamental knowledge to solve specific problems, for example, in the treatment of diseases associated with disorders of the nervous system.

Every day, specialists are faced with the following issues:

1. how the brain and neural networks work at different levels of interaction, from cellular to system levels;

2. how can brain reactions be reliably measured;

3. what connections, functional, anatomical and genetic, can be traced in the work of neurons at different levels of interaction;

4. which indicators of brain function can be considered diagnostic or prognostic in medicine;

5. what drugs should be developed for the treatment and protection of pathological conditions and neurodegenerative diseases of the nervous system.

How to become a specialist?

Additional education

Find out more about possible career preparation programs while still at school age.

Basic vocational education

Percentages reflect the distribution of specialists with a certain level of education in the labor market. Key specializations for mastering the profession are marked in green.

Abilities and skills

Working with information. Skills in searching, processing and analyzing received information
An integrated approach to problem solving. The ability to see a problem comprehensively, in context and, based on this, select the necessary pool of measures to solve it
Programming. Skills in writing code and debugging it
Observations. Skills in conducting scientific observations, recording the results obtained and analyzing them
Science skills. Ability to apply knowledge in the field of natural sciences when solving professional problems
Research skills. Ability to conduct research, set up experiments, collect data
Math skills. Ability to apply mathematical theorems and formulas when solving professional problems
System assessment. The ability to build a system for assessing any phenomenon or object, select assessment indicators and conduct an assessment based on them

Interests and preferences

Analytical thinking. Ability to analyze and forecast a situation, draw conclusions based on available data, and establish cause-and-effect relationships
Critical thinking. Ability to think critically: weigh the pros and cons, the strengths and weaknesses of each approach to solving a problem and each possible outcome
Mathematical abilities. Ability in mathematics and exact sciences, understanding of the logic of mathematical provisions and theorems
Learning ability. The ability to quickly assimilate new information and apply it in further work
Assimilation of information. The ability to quickly perceive and assimilate new information
Flexibility of thinking. The ability to operate with several rules simultaneously, combine them, and derive the most relevant model of behavior
Openness to new things. Ability to stay abreast of new technical information and work-related knowledge
Visualization. Creation in the imagination of detailed images of those objects that need to be obtained as a result of the work
Organizing information. The ability to organize data, information, and things or actions in a specific order according to a specific rule or set of rules
Attention to details. Ability to concentrate on details when completing tasks
Memory. Ability to quickly remember significant amounts of information

Profession in persons

Olga Martynova

Alexander Surin

The weight of the brain is 3-5% of a person's total weight. And this is the largest brain-to-body weight ratio in the animal kingdom.

You can enter the profession with a technical and mathematical education, since specialists who know complex methods of statistical analysis of large volumes of data and who can work with Big Data are increasingly required.

Neuroscientists can find work in departments of neurology, neuropsychiatry, etc. Moscow city clinics and clinics. In scientific organizations, specialists in the field of neurobiology will increase the level of scientific research into the functioning of the nervous system in health and disease; in medical institutions they will improve the quality of diagnosis of diseases and reduce the time for making diagnoses; will contribute to the development of progressive treatment strategies.

The brain and nervous system as a whole are perhaps the most complex system in the body. 70% of the human genome ensures the formation and functioning of the brain. More than 100 billion cell nuclei are found in the human brain, which is more than the number of stars in the visible region of space.

Today, scientists and doctors have learned to transplant and replace almost any tissue and any organ in the human body. Every day, many kidney, liver, and even heart transplant operations are performed. However, a head transplant operation was successful only once, when the Soviet surgeon V. Demikhov transplanted a second head into a healthy dog. It is known that he conducted many similar experiments on dogs, and in one case such a two-headed creature lived for almost a month. Today, similar experiments are also being carried out on animals; methods are being sought to fuse the brain and spinal cord during transplantation, which is the most important problem in this type of operation, but so far scientists are far from carrying out such operations on humans. Head or brain transplants could help paralyzed people, those who cannot control their bodies, but the question of the ethics of head transplants also remains open.