We implement the Ising model on a structural connectivity matrix describing the brain at a coarse scale. Tuning the model temperature to its critical value, i.e. at the susceptibility peak, we find a maximal amount of total information transfer between the spin variables. At this point the amount of information that can be redistributed by some nodes reaches a limit and the net dynamics exhibits signature of the law of diminishing marginal returns, a fundamental principle connected to saturated levels of production. Our results extend the recent analysis of dynamical oscillators models on the connectome structure, taking into account lagged and directional influences, focusing only on the nodes that are more prone to became bottlenecks of information. The ratio between the outgoing and the incoming information at each node is related to the number of incoming links.
Predicting Brain Morphogenesis via Physics-Transfer Learning
Applied Mechanics Laboratory, Department of Engineering Mechanics, Tsinghua University（清华大学工程力学系应用力学实验室）；Institute of Mechanics and Computational Engineering, Department of Aeronautics and Astronautics & College of Intelligent Robotics and Advanced Manufacturing, Fudan University（复旦大学力学与计算工程研究所）
Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network
Okayama University（冈山大学）；Japan Science and Technology Agency（日本科学技术振兴机构）；Rikkyo University（立教大学）；National Institute for Physiological Sciences（生理学研究所）；Araya Inc.（Araya 公司）；Keio University（庆应义塾大学）
BrainWideBench: Benchmarking large-scale pretraining and across-animal transfer in multi-region neural recordings
University of Pennsylvania（宾夕法尼亚大学）；Mila（米拉研究所）；Université de Montréal（蒙特利尔大学）；Stanford University（斯坦福大学）；Columbia University（哥伦比亚大学）；Allen Institute（艾伦研究所）；William James Center for Research（威廉·詹姆斯研究中心）；ISPA - Instituto Universitário（ISPA 大学研究所）；University of Geneva（日内瓦大学）；Karolinska Institutet（卡罗林斯卡学院）；UCLA（加州大学洛杉矶分校）；University College London（伦敦大学学院）；Champalimaud Foundation（尚帕利莫基金会）；Lingang Laboratory（临港实验室）；The Chinese University of Hong Kong（香港中文大学）；Donders Institute（唐德斯研究所）；University of Minnesota（明尼苏达大学）；Princeton University（普林斯顿大学）；Leiden University（莱顿大学）；McGill University（麦吉尔大学）；IBM
Evaluating deep transfer learning for whole-brain cognitive decoding
Technische Universität Berlin（柏林工业大学）；Max Planck Institute for Human Development（马克斯·普朗克人类发展研究所）；Stanford University（斯坦福大学）；Max Planck UCL Centre for Computational Psychiatry and Ageing Research（马克斯·普朗克伦敦大学学院计算精神病学与老龄化研究中心）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；Korea University（高丽大学）；Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
