What's the difference between expert and novice brains
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Last Update: 2019-11-26
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Source: Internet
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Author: User
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What's the difference between the brain of an expert and that of a beginner? Recently, researchers have discovered the difference between animal experiments and machine learning algorithms, and developed new methods to predict animal behavior, which also shows the potential of artificial intelligence to help neuroscience research Researchers from colder spring laboratory, Columbia University and London University College in the United Kingdom have found that when mice change from "novice" to "expert", their brain cell circuits and neuron activities will change, and the neural network will become more concentrated and focused The research results were recently published in the US monthly neuron In the experiment, the researchers trained the mice to complete the cognitive decision-making task The mice first received multiple sensory stimuli, such as flashes and ticks, and then, by selectively licking one of the three outlets, told the researchers whether the stimuli were high or low frequency If the selection is correct, the mice will be rewarded During this process, the neuron changes of mice were recorded by two-photon imaging microscope The researchers found that at the beginning of learning, the relevant neurons would only give feedback at the moment of making a choice; when the mouse fully mastered the skill, it was like "having a hand in the chest", and its neurons would start to give feedback a long time in advance In order to "read" the neural activities, the researchers trained an artificial neural network using machine learning algorithm This artificial neural network named "linear support vector machine" collects a number of experimental data and associates them with the activity of neurons The researchers found that as the mice completed their tasks more skillfully, their neural network became more accurate and focused When these changes were mapped into the artificial neural network, they could predict the next behavior of the mice, with an accuracy of up to 90%
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