Brain and nerve = Shinkei kenkyū no shinpo
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Artificial intelligence and brain science have kept a swinging relationship with opposing views: "Artificial realization of intelligence should be free from biological constraints" and "We should reverse-engineer the best existing implementation of intelligence." In this article, we first review today's achievements of artificial intelligence and its impacts on brain and life sciences. We then discuss how progresses in brain science can contribute to future developments in artificial intelligence.
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Half a century ago, cerebellar learning models based on a simple perceptron were proposed independently by Marr and Albus. Soon, these models were combined with Ito's flocculus hypothesis that the cerebellar flocculus controls the vestibulo-ocular reflex through teacher signal-dependent learning, and consequently integrated into the so-called Marr-Albus-Ito cerebellar learning hypothesis. Ten years later, Ito found the synaptic plasticity of long-term depression at cerebellar Purkinje cell synapses, which underlies cerebellar learning. ⋯ Artificial intelligence (AI) based on the neural network models originating from a simple perceptron, has now developed to deep learning. As the LSM model of the cerebellum is the counterpart of deep learning in the brain, the cerebellum is considered to be the origin of current AI. Finally, we discuss the impact of the evolution of AI on future clinical cerebellar neurology.