https:\/\/arxiv.org\/abs\/2103.01636<\/a> (2021).<\/li>\n<\/ul>\n\n\n\nThe BlueNN group short-term research interest is to conceive scalable deep artificial neural network models and their corresponding learning algorithms using principles from network science, evolutionary computing, optimization and neuroscience. Such models shall have sparse and evolutionary connectivity, make use of previous knowledge, and have strong generalization capabilities to be able to learn, and to reason, using few examples in a continuous and adaptive manner.<\/p>\n\n\n\n
Most science carried out throughout human evolution uses the traditional reductionism paradigm, which even if it is very successful, still has some limitations. Aristotle wrote in Metaphysics \u201cthe totality is not, as it were, a mere heap, but the whole is something beside the parts<\/em>\u201d. Inspired by this quote, in long term, the BlueNN group would like to follow the alternative complex systems paradigm and study the synergy between artificial intelligence, neuroscience, and network science for the benefits of science and society.<\/p>\n\n\n\n