Synaptic Number Intelligence
There was a certain relationship between the number of Synapses and intelligence. A neuroscientist determines the usage rate of the human brain by measuring the number of Synapses on the brain cells. The higher the number of Synapses, the higher the usage rate of brain cells, and the more developed one's intelligence. Some studies have shown that people with a relatively high level of education have more nerve cells in their brains. In addition, people with high intelligence also had stronger neuromalleability. They would produce more nerve Synapses under the same stimulation, thus increasing their brain capacity. However, the number of Synapses is not the only determining factor of intelligence. Other factors such as the complexity of the Synapses, the number of specific protein on the Synapses, and the structure of the brain may also play an important role in the development of intelligence. In general, there was a certain relationship between the number of Synapses and intelligence, but it was not a decisive factor.
Synaptic hyperpolar
Synaptic hypersaturation refers to the phenomenon of hypersaturation of the post-Synaptic membrane under the effect of neurotransmitter. Hyperpolar refers to the membrane potential becoming more negative, resulting in a decrease in the excitability of the neurons to stimulation. Specifically, when the suppressive neurotransmitter acts on the post-Synaptic membrane, it will cause the Cl-channel on the post-Synaptic membrane to open, allowing Cl-ions to flow in, thus making the membrane potential more negative and reaching a hyperpolar state. This hyperpolar state would suppress the transmission of nerve impulses, making it less likely for the later neurons to produce nerve impulses. Synaptic hyperoscillations play an important role in the inhibition of neurotransmitter regulation, which is of great significance in regulating the information transmission between neurons and the normal function of neural networks.
Synaptic components
The components of the Synapse include the presynaptic membrane, the Synaptic Gap, and the post-Synaptic membrane. The presynaptic membrane was the membrane of the Synaptosome, and the post-Synaptic membrane was the membrane of the post-Synaptic neurons that corresponded to the presynaptic membrane. The desynaptic gap was a narrow gap between the presynaptic membrane and the postsynaptic membrane.
synaptic efficacy
Synaptic efficacy referred to the strength and efficiency of the transmission of the nerve. The increase in the effectiveness of the Synaptic Connection could be achieved through the use of Synaptic Membrane, which was the adjustability of the Synaptic Connection. Synaptic elasticity refers to the characteristics of the strength of the Synaptic Connection that can be changed, including formation, elimination, strengthening, and weakening. The mechanisms of the plasticity-inducing effect include the change in the probability of neurotransmitter release at the presynaptic stage, the change in sensitivity to neurotransmitter at the post-Synaptic stage, and the change in the number and structure of the Synaptic System. The long-term enhancement (LTL) and inhibition (ilt) of the effectiveness of the nerve are two forms of the plastic of the nerve. LTL referred to the process of long-term enhancement of the transmission efficiency of the nerve, while ilt referred to the process of long-term weakening of the transmission efficiency of the nerve. The increase in the effectiveness of the Synapse could be caused by the repeated and continuous stimulation of the presynaptic cells on the post-Synaptic cells. This theory was called Hebb's theory. Hebb's theory was that when the presynaptic neurons were repeatedly stimulated to the post-Synaptic neurons, the efficiency of the transmission would increase. Further research was needed to reveal the specific mechanism of the effect of the nerve.
Synaptic mechanism
The formation and regulation mechanism of the Synapse is an important research topic in the field of neuroscience. Some information about the mechanism of the Synapse. First of all, the Synapse was the connection point for information transmission between neurons, and it was highly dynamic. The formation and regulation of the Synapse involves the interaction of multiple protein and molecules. Among them, the protein psd-95 and SynGAP in the post-Synaptic Dense Zone (PSD) could undergo liquid-liquid separation at high concentration, which provided a possible answer to the formation of the Synapse. In addition, Synaptic attachment molecules such as Latrophilin-3 (Lphn3) play an important role in the establishment of the Synapse. It can bind to the tenurin and FLVRT-like attachment molecules in the presynaptic membrane. In addition, the formation and regulation of the Synapse also involved the control of the Trans-Synaptic Complex between the presynaptic membrane and the Post-Synaptic membrane. However, there were still many questions about the specific mechanism of the neural network, and further research was needed to answer them.
Synaptic energy
Synapses were an important structure for transmitting signals between the brain's neurons. Synaptic activity required a large amount of energy to support the function and signal transmission process of the neurons. Synapses consume 20% of the total energy of the human body, and glucose is the main carbon source for the production of ATP. 95% of the ATP-generated in the brain. Synaptic activity requires ATP-driven action potentials, resting potentials, and ion grads. In addition, the circulation of the vesicles and the attachment of the mitochondria to the membrane also required a large amount of energy. The study also found that high-intensity Synaptic Activity would lead to a decrease in presynaptic energy levels and activate the AMPK signaling pathway, thereby increasing the number of mitochondria anchored at the presynaptic level. In short, the metabolism of the energy in the brain was an important process to support the transmission function of the brain.
Synaptic composition
Synapses were composed of presynaptic, desynaptic, and post-desynaptic parts. The presynaptic was the end of the axon of the previous nerve cell, and the post-Synaptic was the cell body or the twig of the next nerve cell. The gap between two neurons is the gap between the two. The presynaptic part included the vesicle and the presynaptic membrane, while the post-synaptic part included the post-synaptic membrane. The structure and composition of the Synapse was the key part of the signal transmission between the neurons.
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Artificial intelligence (AI) is a broad term used to describe applications that perform complex tasks that used to require human input. It includes subfields such as machine learning and deep learning. Machine learning focuses on building systems that can learn or improve performance based on the data they use. The goal of artificial intelligence is to create a self-learning system that can solve problems like humans. Artificial intelligence could be applied to various fields, such as online communication with customers, chess, image recognition, and so on. It also streamlines business processes, improves the customer experience, and speeds up innovation. The development of artificial intelligence had gone through many stages, from general-purpose computing devices to logical reasoning expert systems, to deep learning computing systems and large model computing systems. The current level of artificial intelligence is called narrow artificial intelligence (ANI). It performs well on specific tasks, but it cannot learn new skills or understand the world in depth. Super Artificial Intelligence (ASI) was a postulated future state with intelligence surpassing human intelligence. At present, artificial intelligence surpassed humans in some tasks, but still lagged behind in other tasks. The industry played a leading role in the cutting-edge research of artificial intelligence, and the cost of training cutting-edge models was getting higher and higher. In the future, the development of artificial intelligence might bring more breakthroughs and applications.
intelligence assistant
A smart assistant was a software program that relied on artificial intelligence technology to understand and execute voice and text commands through natural language processing. They can perform a variety of tasks, such as reading texts, dictation, making phone calls, etc., and are usually cloud-based and can be accessed anywhere through an Internet connection. Smart assistants can be connected to smart devices to better integrate into the user's daily life, and they can be highly customized through self-learning algorithms. There are many smart assistants available on the market, such as Otter. OtterPilotm, SciChatAI Academic Assistant, Sider, Kimi.ai, and CodeGeeX. These intelligent assistants could provide various functions, such as academic assistance, chat bots, readers, programming assistants, and so on. In addition, some large technology companies and Internet giants are also actively investing resources in the development of smart assistants to occupy an advantageous position in the artificial intelligence market.
Bamboo Intelligence
Takejian Intelligence was an artificial intelligence company that focused on multi-mode emotion recognition and computing. They provide products such as brain-like conversational robots and multi-mode emotion recognition systems. The technical features of Takejian AI included natural language processing, knowledge engineering, deep learning, machine learning platform, intelligent voice technology, and emotion recognition algorithms. Their goal was to make artificial intelligence affordable for all companies. Takejian AI had a wide range of applications in the fields of finance, enterprise, health, manufacturing, and so on. It had also won many honors and awards at home and abroad. Their customers included AI+ finance, AI+ medical medicine, AI+ education, AI+ internet, AI+ smart terminal, and AI+ traditional industries. The headquarters of Bamboo Intelligence was located in Xuhui, Shanghai, with R & D centers in Beijing, Taiwan, and other places. Their team consisted of scientists in various fields, including artificial intelligence, deep learning/machine learning, natural language understanding, data mining, image recognition, voice emotion recognition, and the Internet of Things. For the seventh year in a row, Takeuma Intelligence was recommended as one of the most influential Chinese AI representative companies by the Gartner report. They also received multiple rounds of funding and cooperated with institutions such as the Chinese Academy of Sciences.
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