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

神经科学和人工智能领域有着悠久的双向互动历史。一方面，人工智能系统发展的重要灵感来自对自然智能系统的研究，特别是哺乳动物的新大脑皮层。另一方面，人工智能研究对大脑模型和理论产生了重要的启发。这两个学科交叉的一个中心问题是，感觉知觉是如何在大脑皮层学习的。在深度学习领域，新大脑皮层学习激发了活跃的研究领域，旨在匹配其数据高效增量学习和分布外泛化，但在很大程度上忽视了数十年来神经科学的见解或约束。基于我们对新皮质突触、树突和回路生理学的最新了解进展，ABL-Lab将利用对新皮质回路和功能深层神经网络的生物物理模拟的协同作用，探索对新皮质学习过程的全新视角，为新皮质学习的功能和功能障碍提供洞察，并提供新的灵感来解决深度学习的根本挑战。

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.