Abstract
It is reasonable to say that so far neural networks have performed very well on many specific tasks of reasonable size, but their performance is far from satisfactory when applied to realistic but complex tasks such speech recognition and language processing. Yet the brain can perform these tasks efficiently and effortlessly (seemingly) using its optimized mechanisms. It is believed crucial to discover these mechanisms. In this paper, a neural network model of the isocortex as basic building block of intelligent systems is consolidated. The model incorporates mechanisms extracted from cortical circuit as suggested from the study of neuroanatomy. The learning rule compatible with what is known about synaptic adaptation in the neocortex is introduced. Simulations results, which verify the mathematical proof of the model stability and robustness, are presented.
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Hoang, D.B., James, M.R. (1996). AMI: A model of intelligence. In: Foo, N., Goebel, R. (eds) PRICAI'96: Topics in Artificial Intelligence. PRICAI 1996. Lecture Notes in Computer Science, vol 1114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61532-6_16
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DOI: https://doi.org/10.1007/3-540-61532-6_16
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