The ability to understand and manipulate numbers and quantities emerges during childhood, but the mechanism through which humans acquire and develop this ability is still poorly understood. We explore this question through a model, assuming that the learner is able to pick up and place small objects from, and to, locations of its choosing, and will spontaneously engage in such undirected manipulation. We further assume that the learner's visual system will monitor the changing arrangements of objects in the scene and will learn to predict the effects of each action by comparing perception with a supervisory signal from the motor system. We model perception using standard deep networks for feature extraction and classification, and gradient descent learning. Our main finding is that, from learning the task of action prediction, an unexpected image representation emerges exhibiting regularities that foreshadow the perception and representation of numbers and quantity. These include distinct categories for zero and the first few natural numbers, a strict ordering of the numbers, and a one-dimensional signal that correlates with numerical quantity. As a result, our model acquires the ability to estimate numerosity, i.e. the number of objects in the scene, as well as subitization, i.e. the ability to recognize at a glance the exact number of objects in small scenes. Remarkably, subitization and numerosity estimation extrapolate to scenes containing many objects, far beyond the three objects used during training. We conclude that important aspects of a facility with numbers and quantities may be learned with supervision from a simple pre-training task. Our observations suggest that cross-modal learning is a powerful learning mechanism that may be harnessed in artificial intelligence.
Emergent Language Symbolic Autoencoder (ELSA) with Weak Supervision to Model Hierarchical Brain Networks
Johns Hopkins University（约翰斯·霍普金斯大学）；Microsoft Health AI（微软健康人工智能）；Johns Hopkins School of Medicine（约翰斯·霍普金斯大学医学院）；Malone Center for Engineering in Healthcare, Johns Hopkins University（约翰斯·霍普金斯大学马龙医疗工程中心）
Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
Univ. of Milan（米兰大学）；Università Campus Bio-Medico di Roma（罗马生物医学大学）；Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Roma（意大利国家核物理研究所罗马分部）；Center for Data Science, New York University（纽约大学数据科学中心）；Institute for Advanced Simulation (IAS-6), Jülich Research Centre（于利希研究中心高级模拟研究所（IAS-6））
Emergent Spatiotemporal Dynamics in Large-Scale Brain Networks with Next Generation Neural Mass Models
Universitat de les Illes Balears（巴利阿里群岛大学）；Universitat Politècnica de Catalunya（加泰罗尼亚理工大学）；Institut de Matemàtiques de la UPC - Barcelona Tech (IMTech)（巴塞罗那理工大学数学研究所）；Centre de Recerca Matemàtica（数学研究中心）
On the robustness of the emergent spatiotemporal dynamics in biophysically realistic and phenomenological whole-brain models at multiple network resolutions
Technische Universität Berlin（柏林工业大学）；University Medicine, Greifswald（格赖夫斯瓦尔德大学医学中心）；German Center for Neurodegenerative Diseases（德国神经退行性疾病中心）；Bernstein Center for Computational Neuroscience（伯恩斯坦计算神经科学中心）；Einstein Center for Neuroscience（爱因斯坦神经科学中心）
Emergent Neural Network Mechanisms for Generalization to Objects in Novel Orientations
Fujitsu Research of America（美国富士通研究院）；The Institute for Artificial Intelligence（人工智能研究院）；Weizmann Institute of Science（魏茨曼科学研究所）；Center for Brains, Minds and Machines（脑、心智与机器中心）；Massachusetts Institute of Technology（麻省理工学院）；Fujitsu Limited（富士通株式会社）
Emergent togetherness in collaborative dance improvisation: neural and motor synchronization reveal a coupling-decoupling paradox
Physics Institute, Federal University of Bahia（巴伊亚联邦大学物理研究所）；Physics Department, Federal University of Pernambuco（伯南布哥联邦大学物理系）；Superior School of Physical Education, University of Pernambuco（伯南布哥大学高等体育教育学院）；Faculty of Human Kinetics, University of Lisbon（里斯本大学人类动力学学院）；Institute of Ethnomusicology - Centre for Studies in Music and Dance（民族音乐学研究所 - 音乐与舞蹈研究中心）；Dance School, Federal University of Bahia（巴伊亚联邦大学舞蹈学院）；Department of Performing Arts, Federal University of Rio de Janeiro（里约热内卢联邦大学表演艺术系）
