Significant brain science

brain science is a fascinating life science, it is full of mystery, and everyone's life is closely related. How does consciousness come about? Why do people have aspirations? Why are some people smarter and have better memories? Why do some children have autistic tendencies? Why do some people suffer from depression? How can robots possess human intelligence and abilities? How will human society coexist with intelligent robots in the future? These problems can be said to be closely related to brain science. Brain science research can not only make us understand the principles of brain function such as cognition, thinking, consciousness and language, which is of great scientific significance to human understanding, but also can analyze the neural basis of various brain functions, and has important clinical significance for the effective diagnosis and treatment of brain diseases.the
human brain is a very complex biological system with hundreds of billions of nerve cells (neurons); neurons have complex neural fiber connections that form neural networks and neural loops that dominate various brain functions through millions of connection points (synapses). For more than a century, the main advance in brain science research has been to understand the fundamentals of the transmission and processing of information in neural networks. In recent years, with the addition of new techniques in molecular biology and physiology, we have also understood the molecular and cellular mechanisms of gene expression, neuron differentiation, and nerve joint formation in brain development, and analyzed the laws of neural loops and electrical activity related to brain function. Due to the complexity of brain structure and dynamics, more new technologies are needed in the future to observe large groups of neurons in different brain regions and regulate their electrical activity;
To understand why the brain is able to think, it's not just about observing what neurons and loops are electrically active when thinking, but also about why they have electrical activity, whether that's the cause or the result of thinking. From correlation to causation, it is necessary to be able to regulate (activate or suppress) their electrical activity to see if it affects the thinking phenomenon. The study of causal relationship between brain electrical activity and cognitive behavior is the main frontier of brain science.neural networks in the brain have a very environmentally friendly feature, which is plasticity. Stimuli from the environment, including interactions with other people's brains, constantly alter the brain's neural networks. From the moment we are born, the brain begins to explore the surrounding world and environment, and information continues to shape the structure and function of the brain so that we can adapt to the needs of the environment. Neural networks during development are highly malleable. As a result, environmentally induced electrical activity can lead to the growth, consolidation, and pruning of neural networks -- preserving suitable and useful connections and cutting out redundant, useless connections. Therefore, each person's different growth experiences are stored in the structure of neural networks, resulting in personality and cognitive abilities that vary from person to person. It is worth noting that the formation and pruning of the brain's neural networks are completed at a critical stage in the first few years of life, and that infercication education is more important to a person's intellectual development than after school.
so-called "gene important or environmentally important?" "It's a misleading question. Genetically based because the brain neural network provides the necessary structural material, genetic abnormalities do cause abnormal networks associated with mental retardation, autism, schizophrenia, but without the shaping of the environment, neural network structure can not be formed. Therefore, genes and the environment are very important. With the exception of a very small number of people due to genetic mutations caused by neural structure original problems, the vast majority of human originals are normal, so the difference in intelligence comes from the development of different environments, thus shaping into different neural networks. Understanding the developmental laws and working mechanisms of neural networks in the brain can help us understand the neural basis of thinking, intelligence, and creativity, and inspire us to design educational models that contribute to the development of intelligence and innovation.
the plasticity of the neural network is not limited to the development period, the adult brain network still maintains a considerable plasticity, the input of environmental information can change the structure and function of the network, which is also the adult brain to carry out various cognitive functions necessary mechanisms. Limited plasticity preserves the imprint of early developmental experience and learning, while still enabling the brain to further learn to remember and adapt to new environments. Although plasticity will gradually decline in adults, resulting in a gradual decline in cognitive ability, but the so-called "live to old, learn to old" still makes sense, because limited neural network plasticity still exists.brain disease and brain trauma are between the most serious problems facing the social health care and health care system. According to the World Health Organization, the burden of social care associated with brain diseases is the highest of all diseases, surpassing cardiovascular disease and cancer. In the case of Alzheimer's disease, for example, more than one-third of people between the ages of 90 and 94 develop the disease. With the growing proportion of older persons in society, social care and health systems will be difficult to afford in decades to come without effective prevention and treatment drugs.
all serious brain diseases, there is currently a lack of effective drugs. Therefore, for the medical treatment of brain diseases, there is a consensus among the brain science and medical community that early diagnosis and early intervention should be initiated. How can brain diseases be diagnosed early? Genetic and non-hereditary genetic variants and environmental factors can lead to brain disease. A small number of brain diseases such as intellectual disability, autism, Huntington's disease and other major disease-caused genes have been identified, but most brain diseases are genetically derived from a variety of susceptible gene variants, which interact with environmental factors to produce brain diseases. As a result, the current contribution of genetic testing to the diagnosis of most brain diseases remains limited. Molecular, structural and functional brain imaging data related to brain diseases in blood and cerebrospinal fluid, and quantitative testing of various cognitive functions may provide indicators for early diagnosis of brain disease, but long-term data collection and analysis are still to be collected and analyzed in large numbers of people.
this explains why there has been no significant progress in the field of brain disease diagnosis and treatment and drug development for many years. First of all, the current diagnosis of brain diseases, especially mental illness diagnostic standards have serious defects. For example, most psychiatrist physicians use diagnostic and statistical manuals developed by European and American physicians to diagnose diseases based on patient symptoms and questionnaire patterns. However, the symptoms of many brain diseases overlap, symptom indicators and disease classification are still controversial, the subjective components of diagnosis are larger. If doctors can't diagnose the type of disease accurately, how can they develop an accurate treatment? Secondly, the cause of brain disorders is often the special neural loop in charge of the function of the problem, but the general drug target lack of loop specificity, will produce a variety of side effects. In addition, people with brain diseases are usually treated with obvious symptoms, such as Parkinson's disease and Alzheimer's disease, and there is little chance of a cure if the neuron is dead and the synapse connection is lost. So early diagnosis and early intervention of brain disease are needed. The diagnostic criteria and treatment of future brain diseases should be specific to the abnormality of the specific neural loop. Therefore, the analysis of the neural loop of brain function in brain science will be more and more important for the diagnosis and treatment of brain diseases. Decades from now, the classification of neurological or psychiatric disorders currently in use may disappear and be replaced by diagnosis and prescription of neural loop abnormalities.
the nervous system after trauma (e.g. stroke, spinal cord injury), the brain region near the trauma area plasticity will increase significantly, which is also the evolution of screening in favor of biological repair network mechanisms. By targeting the nervous system, physical, physical, and psychological stimulation can help the damaged loop regenerate or establish a new loop, restore the normal function of the network. This is the main goal of brain disease and brain trauma rehabilitation. Therefore, brain science analyzes the neural loops associated with various brain functions, which can help us determine the neural loops that rehabilitation medicine should target. For example, when designing the brain-computer interface of a "closed-loop" rehabilitation device, we not only need to manipulate the device with the information generated by the brain, but also give back information that reflects the working state of the device to the brain, further correcting the brain's output information. The current difficulty in this area is how to effectively parse the brain's information and to be able to enter feedback information into the brain with a clear target. The field of brain-computer interface is the key field of the close integration of human brain and machine in the future, which will provide a wide range of applications for human brain controller parts and devices to regulate human brain state.artificial intelligence is the most concerned area of science and technology, it appears in order to achieve human brain intelligence with machine intelligence, and the most important characteristic of the human brain is to be able to learn, after learning to remember. So machine learning is at the heart of artificial intelligence. Artificial intelligence includes machine learning algorithms that efficiently process information and software and hardware computing systems that utilize them. Machine learning has become much more efficient in recent years because of the large number of known (taggable) data samples and powerful computer capabilities. A variety of deep learning algorithms, that is, the use of tagged data specimens to let people work network to learn, the learning process is to mark the data into the network, and then according to whether the output of the network conforms to the mark, to the network's neural join strength is constantly corrected. In multi-layer deep neural networks there are a large number of join points (synapses) can be modified, and a large number of marked data can be used to modify, so that the well-learned network has a strong ability to identify unknown data. Learned human networks can be represented in the hardware architecture of a computer for information processing.
introduced new learning algorithms in the 1980s, effectively modifying synact nodes in multi-layered artificial networks, making artificial neural networks the main carrier of machine learning. But the structure of the artificial network is still too simple relative to the neural network of the human brain, and its number of multi-layered neural units can exceed that of the human brain, but the ability to learn and process diverse information is far less than that of the human brain, let alone complex cognitive functions. Although the understanding of the neural loop and working function of many cognitive functions is very limited, we already know that some of the structure and functional principles of brain neural networks can be applied to the architecture of machine learning algorithms and computational devices, which is brain science-inspired brain-like artificial intelligence. Brain-like research is not simulating the human brain, but applying the principles of brain science to the hardware and software of artificial intelligence.
current artificial intelligence technology lacks versatility. Speech recognition, image processing, natural language processing, machine translation, etc. use different models and different learning data, two different tasks can not use the same system to learn. However, the human brain uses the same information processing system for automatic multimodal perception information integration, problem analysis and solution, decision-making and behavior control. The key to the sustainable development of artificial intelligence lies in whether machine learning algorithms have broken through, from relying on tagged big data and high computing power to learning small and multimodal data without tagging, and to be energy efficient. This is the key to moving from dedicated artificial intelligence to universal artificial intelligence. Who can first develop these next-generation machine learning algorithms and implement them in computing devices, and who can do it in the future of artificial intelligence?
(Author is
Director,
Center for Excellence and Innovation in Brain Science and Intelligent Technology, China)