A new paradigm for perceptual learning in neocortical dendrites and deep neural networks

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The fields of neuroscience and artificial intelligence have a long history of fertile bi-directional interactions. On the one hand, important inspiration for the development of artificial intelligence systems has come from the study of natural systems of intelligence, the mammalian neocortex in particular. On the other, important inspiration for models and theories of the brain have emerged from artificial intelligence research. A central question at the intersection of these two disciplines is how sensory perception is learned in the neocortex. In the field of deep learning, neocortical learning inspires active areas of research aiming at matching its data efficient incremental learning and out-of-distribution generalization, but largely ignoring decades of neuroscientific insights or constraints. Building on recent advances in our understanding of the synaptic, dendritic and circuit physiology of the neocortex, the ABL-Lab will leverage a synergy of biophysical simulations of neocortical circuits and functional deep neural networks to explore a radically new perspective on the processes of learning in the neocortex, providing insight into the function and dysfunction of neocortical learning, and also new inspiration to solve fundamental challenges for deep learning.