The Experts below are selected from a list of 33426 Experts worldwide ranked by ideXlab platform
Anatoli Gorchetchnikov - One of the best experts on this subject based on the ideXlab platform.
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grid cell hexagonal patterns formed by fast self organized learning within entorhinal cortex
Hippocampus, 2012Co-Authors: Himanshu Mhatre, Anatoli GorchetchnikovAbstract:Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general Mathematical Property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. © 2010 Wiley Periodicals, Inc.
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grid cell hexagonal patterns formed by fast self organized learning within entorhinal cortex
Hippocampus, 2012Co-Authors: Himanshu Mhatre, Anatoli Gorchetchnikov, Stephen GrossbergAbstract:Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general Mathematical Property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. © 2010 Wiley Periodicals, Inc.
Himanshu Mhatre - One of the best experts on this subject based on the ideXlab platform.
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grid cell hexagonal patterns formed by fast self organized learning within entorhinal cortex
Hippocampus, 2012Co-Authors: Himanshu Mhatre, Anatoli GorchetchnikovAbstract:Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general Mathematical Property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. © 2010 Wiley Periodicals, Inc.
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grid cell hexagonal patterns formed by fast self organized learning within entorhinal cortex
Hippocampus, 2012Co-Authors: Himanshu Mhatre, Anatoli Gorchetchnikov, Stephen GrossbergAbstract:Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general Mathematical Property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. © 2010 Wiley Periodicals, Inc.
Yutaka Akagi - One of the best experts on this subject based on the ideXlab platform.
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topological invariant for bosonic bogoliubov de gennes systems with disorder
Bulletin of the American Physical Society, 2021Co-Authors: Yutaka AkagiAbstract:Using the method of noncommutative geometry, we define a topological invariant in disordered bosonic Bogoliubov-de Gennes systems, which possess a unique Mathematical Property---non-Hermiticity. To demonstrate the validity of the definition, we investigate a disordered artificial spin ice model in two dimensions numerically. In the clean limit, we clarify that the topological index perfectly coincides with the Chern number. We also show that the topological index is robust against disorder. The formula provides the topological index $n_{\rm Ch}=1$ in the magnon Hall regime and $n_{\rm Ch}=0$ in a trivial localized one. We also show by example that our method can be extended to other symmetry classes. Our results pave the way for further studies on topological bosonic systems with disorder.
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topological invariant for bosonic bogoliubov de gennes systems with disorder
Journal of the Physical Society of Japan, 2020Co-Authors: Yutaka AkagiAbstract:Using the method of noncommutative geometry, we define a topological invariant in disordered bosonic Bogoliubov–de Gennes systems, which possess a unique Mathematical Property — non-Hermiticity. To...
Stephen Grossberg - One of the best experts on this subject based on the ideXlab platform.
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grid cell hexagonal patterns formed by fast self organized learning within entorhinal cortex
Hippocampus, 2012Co-Authors: Himanshu Mhatre, Anatoli Gorchetchnikov, Stephen GrossbergAbstract:Grid cells in the dorsal segment of the medial entorhinal cortex (dMEC) show remarkable hexagonal activity patterns, at multiple spatial scales, during spatial navigation. It has previously been shown how a self-organizing map can convert firing patterns across entorhinal grid cells into hippocampal place cells that are capable of representing much larger spatial scales. Can grid cell firing fields also arise during navigation through learning within a self-organizing map? This article describes a simple and general Mathematical Property of the trigonometry of spatial navigation which favors hexagonal patterns. The article also develops a neural model that can learn to exploit this trigonometric relationship. This GRIDSmap self-organizing map model converts path integration signals into hexagonal grid cell patterns of multiple scales. GRIDSmap creates only grid cell firing patterns with the observed hexagonal structure, predicts how these hexagonal patterns can be learned from experience, and can process biologically plausible neural input and output signals during navigation. These results support an emerging unified computational framework based on a hierarchy of self-organizing maps for explaining how entorhinal-hippocampal interactions support spatial navigation. © 2010 Wiley Periodicals, Inc.
Victor O.k. Li - One of the best experts on this subject based on the ideXlab platform.
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a novel interpolation svt approach for recovering missing low rank air quality data
IEEE Access, 2020Co-Authors: Yangwen Yu, James J Q Yu, Victor O.k. LiAbstract:With increasing public demands for timely and accurate air pollution reporting, more air quality monitoring stations have been deployed by the governments in urban metropolises to increase the coverage of urban air pollution monitoring. However, due to systematic or accidental failures, some air pollution measurements obtained from these stations are found to have missing values, which will adversely affect the accuracy of any follow-up air pollution analyses and the quality of environmental decision-makings. In this study, the Mathematical Property of air quality measurements is investigated to recover the missing air pollution values. A new algorithm, which matches meteorology data with air pollution data from different locations, to reconstruct the data matrix and recover missing entries, is proposed. Next, a Low Rank Matrix Completion problem is used to reconstruct the missing values, by transforming the data recovery problem to a sub-gradient primal-dual problem, based on the duality theory, with Singular Value Thresholding (SVT) employed to develop sub-optimal solutions. Next, an Interpolation-SVT (ISVT) approach is adopted to handle the sparsity of observed measurements. Comprehensive case studies are conducted to evaluate the performance of the proposed methods. The simulation results have demonstrated that the proposed SVT and ISVT methods can effectively recover the missing air pollution data and outperform existing interpolation methods and data imputation techniques. The proposed study can improve air pollution estimation and prediction whenever the low-rank data types that are used as proxies for air pollution estimation contain a lot of missing values and require data recovery.
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low rank singular value thresholding for recovering missing air quality data
International Conference on Big Data, 2017Co-Authors: Yangwen Yu, James J Q Yu, Victor O.k. LiAbstract:With the increasing awareness of the harmful impacts of urban air pollution, air quality monitoring stations have been deployed in many metropolitan areas. These stations provide air quality data to the public. However, due to sampling device failures and data processing errors, missing data in air quality measurements is common. Data integrity becomes a critical challenge when such data are employed for public services. In this paper, we investigate the Mathematical Property of air quality measurements, and attempt to recover the missing data. First, we empirically study the low rank Property of these measurements. Second, we formulate the low rank matrix completion (LRMC) optimization problem to reconstruct the missing air quality data. The problem is transformed using duality theory, and singular value thresholding (SVT) is employed to develop sub-optimal solutions. Third, to evaluate the performance of our methodology, we conduct a series of case studies including different types of missing data patterns. The simulation results demonstrate that the proposed SVT methodology can effectively recover missing air quality data, and outperform the existing Interpolation. Finally, we investigate the parameter sensitivity of SVT. Our study can serve as a guideline for missing data recovery in the real world.