Computations on the dendritic trees of neurons have important constraints. Voltage dependent conductances in dendrites are not similar to arbitrary direct-current generation, they are the basis for dendritic nonlinearities and they do not allow converting positive currents into negative currents. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these biological constraints. Here we simulate models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by these constraints but may benefit from them. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks.
Leveraging dendritic properties to advance machine learning and neuro-inspired computing
Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology Hellas (FORTH)（希腊研究与技术基金会分子生物学与生物技术研究所）；University of Crete（克里特大学）
Mitigating Communication Costs in Neural Networks: The Role of Dendritic Nonlinearity
Zhejiang Lab（浙江实验室）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Bytedance（字节跳动）；Peking University（北京大学）；National Biomedical Imaging Center, Peking University（北京大学生物医学成像中心）；Zhejiang University（浙江大学）；IMBB-FORTH（希腊研究与技术基金会生物医学研究学院分子生物学与生物技术研究所）
Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible
Université de Montréal（蒙特利尔大学）；Montreal Institute for Learning Algorithms（蒙特利尔学习算法研究所）；University of Bern（伯尔尼大学）；Canadian Institute for Advanced Research（加拿大高等研究院）
NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integration
Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, China-Cuba Belt and Road Joint Laboratory on Neurotechnology and Brain-Apparatus Communication, School of Life Science and Technology, University of Electronic Science and Technology of China（成都脑科学研究所临床医院，教育部神经信息重点实验室，中老一带一路神经技术与脑-装置通信联合实验室，生命科学与技术学院，电子科技大学）；Academy of Medical Engineering and Translational Medicine, Tianjin University（医学工程与转化医学学院，天津大学）；School of Artificial Intelligence, Chongqing University of Education（人工智能学院，重庆教育学院）；Research Unit of NeuroInformation (2019RU035)（神经信息研究单位（2019RU035））
Field closure, ice neurons, and when a dendrite is a motif
McGovern Institute for Brain Research, Massachusetts Institute of Technology（麻省理工学院麦戈文脑研究所）；The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI)（美国国家科学基金会人工智能基础相互作用研究所）
The role of gain neuromodulation in layer-5 pyramidal neurons
Neural Circuits Laboratory, Biosciences Institute, Faculty of Medical Sciences, Newcastle University（神经回路实验室、生物科学学院、医学科学学院、新castle大学）；Brain and Mind Center, The University of Sydney（脑与心智中心、悉尼大学）；Center for Complex Systems, The University of Sydney（复杂系统中心、悉尼大学）；IT:U Interdisciplinary Transformation University Austria（奥地利 interdisciplinary transformation university Austria）；International Research Center for Neurointelligence, The University of Tokyo（神经智能国际研究中心、东京大学）；Department of Anatomy, University of Quebec in Trois-Rivieres（解剖学系、魁北克三河大学）；Theoretical Sciences Visiting Program (TSVP), Okinawa Institute of Science and Technology Graduate University（理论科学访问计划（TSVP）、冲绳科学技术大学院大学）
Visual attention information can be traced on cortical response but not on the retina: evidence from electrophysiological mouse data using natural images as stimuli
