A brain-inspired framework for evolutionary artificial general intelligence
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M. Nadji-Tehrani and A. Eslami, "A Brain-Inspired Framework for Evolutionary Artificial General Intelligence," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 12, pp. 5257-5271, Dec. 2020, doi: 10.1109/TNNLS.2020.2965567.
From the medical field to agriculture, from energy to transportation, every industry is going through a revolution by embracing artificial intelligence (AI); nevertheless, AI is still in its infancy. Inspired by the evolution of the human brain, this article demonstrates a novel method and framework to synthesize an artificial brain with cognitive abilities by taking advantage of the same process responsible for the growth of the biological brain called “neuroembryogenesis.” This framework shares some of the key behavioral aspects of the biological brain, such as spiking neurons, neuroplasticity, neuronal pruning, and excitatory and inhibitory interactions between neurons, together making it capable of learning and memorizing. One of the highlights of the proposed design is its potential to incrementally improve itself over generations based on system performance, using genetic algorithms. A proof of concept at the end of this article demonstrates how a simplified implementation of the human visual cortex using the proposed framework is capable of character recognition. Our framework is open source, and the code is shared with the scientific community at http://www.feagi.org.
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