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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:22/0  |  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.


  
Machine Learning Approach to Characterize the Postseismic Deformation of the 2011 Tohoku-Oki Earthquake Based on Recurrent Neural Network 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2019, 46 (21) : 11886-11892
作者:  Yamaga, Norifumi;  Mitsui, Yuta
收藏  |  浏览/下载:19/0  |  提交时间:2020/02/17
Machine learning  Recurrent neural network  GNSS  2011 Tohoku-oki earthquake  Postseismic deformation  Regression analysis  
Deep Learning Models Augment Analyst Decisions for Event Discrimination 期刊论文
GEOPHYSICAL RESEARCH LETTERS, 2019, 46 (7) : 3643-3651
作者:  Linville, Lisa;  Pankow, Kristine;  Draelos, Timothy
收藏  |  浏览/下载:13/0  |  提交时间:2019/11/26
Utah  event classification  event discrimination  deep learning  convolutional neural network  recurrent neural network