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Join us in this thought-provoking episode with Dr. Dumas, where we explore the fascinating world of the developing brain and its cognitive abilities. Dr. Dumas presents a neurocomputational model of the developing brain, outlining three tasks of increasing complexity, from visual recognition to cognitive manipulation and conscious percept management. She emphasizes two crucial mechanisms in the development of cognitive abilities in biological neural networks: synaptic epigenesis and self-organized dynamics. Dr. Dumas explains the concept of synaptic epigenesis, which involves Hebbian learning at the local scale and reinforcement learning at the global scale. She also elaborates on self-organized dynamics, emphasizing the role of spontaneous activity and a balanced excitatory/inhibitory ratio in neurons. The conversation further explores the implications of these findings on future developments in artificial intelligence, underlining the core features of human intelligence that could be used as guiding principles.
Keywords: Cognitive Abilities, Neurocomputational Model, Synaptic Epigenesis, Self-Organized Dynamics, Hebbian Learning, Reinforcement Learning, Artificial Intelligence, Human Intelligence, Neurodevelopmental.
Multilevel development of cognitive abilities in an artificial neural network https://doi.org/10.1073/pnas.2201304119 This could guide future development in artificial intelligence.
By Catarina CunhaJoin us in this thought-provoking episode with Dr. Dumas, where we explore the fascinating world of the developing brain and its cognitive abilities. Dr. Dumas presents a neurocomputational model of the developing brain, outlining three tasks of increasing complexity, from visual recognition to cognitive manipulation and conscious percept management. She emphasizes two crucial mechanisms in the development of cognitive abilities in biological neural networks: synaptic epigenesis and self-organized dynamics. Dr. Dumas explains the concept of synaptic epigenesis, which involves Hebbian learning at the local scale and reinforcement learning at the global scale. She also elaborates on self-organized dynamics, emphasizing the role of spontaneous activity and a balanced excitatory/inhibitory ratio in neurons. The conversation further explores the implications of these findings on future developments in artificial intelligence, underlining the core features of human intelligence that could be used as guiding principles.
Keywords: Cognitive Abilities, Neurocomputational Model, Synaptic Epigenesis, Self-Organized Dynamics, Hebbian Learning, Reinforcement Learning, Artificial Intelligence, Human Intelligence, Neurodevelopmental.
Multilevel development of cognitive abilities in an artificial neural network https://doi.org/10.1073/pnas.2201304119 This could guide future development in artificial intelligence.