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神经网络可以帮助预测破坏性地震 快报文章
地球科学快报,2023年第06期
作者:  王立伟
Microsoft Word(14Kb)  |  收藏  |  浏览/下载:472/0  |  提交时间:2023/03/24
Neural network  predict  destructive earthquakes  
Development of advanced artificial intelligence models for daily rainfall prediction 期刊论文
ATMOSPHERIC RESEARCH, 2020, 237
作者:  Binh Thai Pham;  Lu Minh Le;  Tien-Thinh Le;  Kien-Trinh Thi Bui;  Vuong Minh Le;  Hai-Bang Ly;  Prakash, Indra
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/02
Rainfall  Artificial Neural Networks  Robustness analysis  Support Vector Machines  Adaptive Network based Fuzzy Inference System  Particle Swarm Optimization  
A map of object space in primate inferotemporal cortex 期刊论文
NATURE, 2020, 583 (7814) : 103-+
作者:  Wu, Huihui;  Li, Bosheng;  Iwakawa, Hiro-oki;  Pan, Yajie;  Tang, Xianli;  Ling-hu, Qianyan;  Liu, Yuelin;  Sheng, Shixin;  Feng, Li;  Zhang, Hong;  Zhang, Xinyan;  Tang, Zhonghua;  Xia, Xinli;  Zhai, Jixian;  Guo, Hongwei
收藏  |  浏览/下载:47/0  |  提交时间:2020/07/03

Primate inferotemporal cortex contains a coarse map of object space consisting of four networks, identified using functional imaging, electrophysiology and deep networks.


The inferotemporal (IT) cortex is responsible for object recognition, but it is unclear how the representation of visual objects is organized in this part of the brain. Areas that are selective for categories such as faces, bodies, and scenes have been found(1-5), but large parts of IT cortex lack any known specialization, raising the question of what general principle governs IT organization. Here we used functional MRI, microstimulation, electrophysiology, and deep networks to investigate the organization of macaque IT cortex. We built a low-dimensional object space to describe general objects using a feedforward deep neural network trained on object classification(6). Responses of IT cells to a large set of objects revealed that single IT cells project incoming objects onto specific axes of this space. Anatomically, cells were clustered into four networks according to the first two components of their preferred axes, forming a map of object space. This map was repeated across three hierarchical stages of increasing view invariance, and cells that comprised these maps collectively harboured sufficient coding capacity to approximately reconstruct objects. These results provide a unified picture of IT organization in which category-selective regions are part of a coarse map of object space whose dimensions can be extracted from a deep network.


  
Hair-bearing human skin generated entirely from pluripotent stem cells 期刊论文
NATURE, 2020
作者:  von Appen, Alexander;  LaJoie, Dollie;  Johnson, Isabel E.;  Trnka, Michael J.;  Pick, Sarah M.;  Burlingame, Alma L.;  Ullman, Katharine S.;  Frost, Adam
收藏  |  浏览/下载:52/0  |  提交时间:2020/07/03

Skin organoids generated in vitro from human pluripotent stem cells form complex, multilayered skin tissue with hair follicles, sebaceous glands and neural circuitry, and integrate with endogenous skin when grafted onto immunocompromised mice.


The skin is a multilayered organ, equipped with appendages (that is, follicles and glands), that is critical for regulating body temperature and the retention of bodily fluids, guarding against external stresses and mediating the sensation of touch and pain(1,2). Reconstructing appendage-bearing skin in cultures and in bioengineered grafts is a biomedical challenge that has yet to be met(3-9). Here we report an organoid culture system that generates complex skin from human pluripotent stem cells. We use stepwise modulation of the transforming growth factor beta (TGF beta) and fibroblast growth factor (FGF) signalling pathways to co-induce cranial epithelial cells and neural crest cells within a spherical cell aggregate. During an incubation period of 4-5 months, we observe the emergence of a cyst-like skin organoid composed of stratified epidermis, fat-rich dermis and pigmented hair follicles that are equipped with sebaceous glands. A network of sensory neurons and Schwann cells form nerve-like bundles that target Merkel cells in organoid hair follicles, mimicking the neural circuitry associated with human touch. Single-cell RNA sequencing and direct comparison to fetal specimens suggest that the skin organoids are equivalent to the facial skin of human fetuses in the second trimester of development. Moreover, we show that skin organoids form planar hair-bearing skin when grafted onto nude mice. Together, our results demonstrate that nearly complete skin can self-assemble in vitro and be used to reconstitute skin in vivo. We anticipate that our skin organoids will provide a foundation for future studies of human skin development, disease modelling and reconstructive surgery.


  
A Machine-Learning Approach to Derive Long-Term Trends of Thermospheric Density 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2020, 47 (6)
作者:  Weng, Libin;  Lei, Jiuhou;  Zhong, Jiahao;  Dou, Xiankang;  Fang, Hanxian
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/02
thermospheric density  Long-term trend  Artificial Neural Network method  solar activity  
Local and global consequences of reward-evoked striatal dopamine release 期刊论文
NATURE, 2020, 580 (7802) : 239-+
作者:  Wagner, Felix R.;  Dienemann, Christian;  Wang, Haibo;  Stuetzer, Alexandra;  Tegunov, Dimitry;  Urlaub, Henning;  Cramer, Patrick
收藏  |  浏览/下载:9/0  |  提交时间:2020/07/03

The neurotransmitter dopamine is required for the reinforcement of actions by rewarding stimuli(1). Neuroscientists have tried to define the functions of dopamine in concise conceptual terms(2), but the practical implications of dopamine release depend on its diverse brain-wide consequences. Although molecular and cellular effects of dopaminergic signalling have been extensively studied(3), the effects of dopamine on larger-scale neural activity profiles are less well-understood. Here we combine dynamic dopamine-sensitive molecular imaging(4) and functional magnetic resonance imaging to determine how striatal dopamine release shapes local and global responses to rewarding stimulation in rat brains. We find that dopamine consistently alters the duration, but not the magnitude, of stimulus responses across much of the striatum, via quantifiable postsynaptic effects that vary across subregions. Striatal dopamine release also potentiates a network of distal responses, which we delineate using neurochemically dependent functional connectivity analyses. Hot spots of dopaminergic drive notably include cortical regions that are associated with both limbic and motor function. Our results reveal distinct neuromodulatory actions of striatal dopamine that extend well beyond its sites of peak release, and that result in enhanced activation of remote neural populations necessary for the performance of motivated actions. Our findings also suggest brain-wide biomarkers of dopaminergic function and could provide a basis for the improved interpretation of neuroimaging results that are relevant to learning and addiction.


Molecular and functional magnetic resonance imaging in the rat reveals distinct neuromodulatory effects of striatal dopamine that extend beyond peak release sites and activate remote neural populations necessary for performing motivated actions.


  
Recurrent interactions in local cortical circuits 期刊论文
NATURE, 2020, 579 (7798) : 256-+
作者:  Liu, Yang;  Nguyen, Phong T.;  Wang, Xun;  Zhao, Yuting;  Meacham, Corbin E.;  Zou, Zhongju;  Bordieanu, Bogdan;  Johanns, Manuel;  Vertommen, Didier;  Wijshake, Tobias;  May, Herman;  Xiao, Guanghua;  Shoji-Kawata, Sanae;  Rider, Mark H.
收藏  |  浏览/下载:8/0  |  提交时间:2020/07/03

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations(1-4). Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average(5,6). Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes(5-7), and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually(8). Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity(7) and photoablating(9,10) neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10-20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.


Computational modelling, imaging and single-cell ablation in layer 2/3 of the mouse vibrissal somatosensory cortex reveals that recurrent activity in cortical neurons can drive input-specific amplification during behaviour.


  
Digital Rock Segmentation for Petrophysical Analysis With Reduced User Bias Using Convolutional Neural Networks 期刊论文
WATER RESOURCES RESEARCH, 2020, 56 (2)
作者:  Niu, Yufu;  Mostaghimi, Peyman;  Shabaninejad, Mehdi;  Swietojanski, Pawel;  Armstrong, Ryan T.
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/02
convolutional neural network  digital rock  image segmentation  X-ray microcomputed tomography  
Neural hierarchical models of ecological populations 期刊论文
ECOLOGY LETTERS, 2020, 23 (4) : 734-747
作者:  Joseph, Maxwell B.
收藏  |  浏览/下载:4/0  |  提交时间:2020/07/02
Deep learning  hierarchical model  neural network  occupancy  
Classification with a disordered dopantatom network in silicon 期刊论文
NATURE, 2020, 577 (7790) : 341-+
作者:  Vagnozzi, Ronald J.;  Maillet, Marjorie;  Sargent, Michelle A.;  Khalil, Hadi;  Johansen, Anne Katrine Z.;  Schwanekamp, Jennifer A.;  York, Allen J.;  Huang, Vincent;  Nahrendorf, Matthias;  Sadayappan, Sakthivel;  Molkentin, Jeffery D.
收藏  |  浏览/下载:24/0  |  提交时间:2020/07/03

Classification is an important task at which both biological and artificial neural networks excel(1,2). In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable(3,4), simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density(5), inherent parallelism and energy efficiency(6,7). However, existing approaches either rely on the systems'  time dynamics, which requires sequential data processing and therefore hinders parallel computation(5,6,8), or employ large materials systems that are difficult to scale up(7). Here we use a parallel, nanoscale approach inspired by filters in the brain(1) and artificial neural networks(2) to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction(9-11) through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates(12) up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters(2) that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data(13). Our results establish a paradigm of silicon-based electronics for smallfootprint and energy-efficient computation(14).