Implementation and evaluation of a self-organizing artificial hormone system to assign time-dependent tasks

2011 ◽  
Vol 24 (16) ◽  
pp. 1879-1902 ◽  
Author(s):  
Mathias Pacher ◽  
Uwe Brinkschulte
Keyword(s):  
Time Dependent ◽  
Dependent Tasks ◽  
Hormone System ◽  
Self Organizing

OPAT: Optimized Allocation of Time Dependent Tasks for Mobile Crowdsensing

2021 ◽  
pp. 1-1
Author(s):  
Yang Huang ◽  
Honglong Chen ◽  
Guoqi Ma ◽  
Kai Lin ◽  
Zhichen Ni ◽  
...  
Keyword(s):  
Time Dependent ◽  
Mobile Crowdsensing ◽  
Allocation Of Time ◽  
Optimized Allocation ◽  
Dependent Tasks

scholarly journals Time-series trend of pandemic SARS-CoV-2 variants visualized using batch-learning self-organizing map for oligonucleotide compositions

2021 ◽  
Author(s):  
Takashi Abe ◽  
Ryuki Furukawa ◽  
Yuki Iwasaki ◽  
Toshimichi Ikemura
Keyword(s):  
Time Series ◽  
New Technologies ◽  
Sequence Data ◽  
Geographic Region ◽  
Time Dependent ◽  
Self Organizing Map ◽  
Population Frequency ◽  
Batch Learning ◽  
Oligonucleotide Composition ◽  
Self Organizing

To confront the global threat of coronavirus disease 2019, a massive number of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome sequences have been decoded, with the results promptly released through the GISAID database. Based on variant types, eight clades have already been defined in GISAID, but the diversity can be far greater. Owing to the explosive increase in available sequences, it is important to develop new technologies that can easily grasp the whole picture of the big-sequence data and support efficient knowledge discovery. An ability to efficiently clarify the detailed time-series changes in genome-wide mutation patterns will enable us to promptly identify and characterize dangerous variants that rapidly increase their population frequency. Here, we collectively analyzed over 150,000 SARS-CoV-2 genomes to understand their overall features and time-dependent changes using a batch-learning self-organizing map (BLSOM) for oligonucleotide composition, which is an unsupervised machine learning method. BLSOM can separate clades defined by GISAID with high precision, and each clade is subdivided into clusters, which shows a differential increase/decrease pattern based on geographic region and time. This allowed us to identify prevalent strains in each region and to show the commonality and diversity of the prevalent strains. Comprehensive characterization of the oligonucleotide composition of SARS-CoV-2 and elucidation of time-series trends of the population frequency of variants can clarify the viral adaptation processes after invasion into the human population and the time-dependent trend of prevalent epidemic strains across various regions, such as continents.

Self-Organizing Dual Coding Based on Spike-Time-Dependent Plasticity

2004 ◽  
Vol 16 (3) ◽  
pp. 627-663 ◽  
Author(s):  
Naoki Masuda ◽  
Kazuyuki Aihara
Keyword(s):  
Neural Networks ◽  
Theoretical Work ◽  
Time Dependent ◽  
Spike Time ◽  
Dependent Plasticity ◽  
Synaptic Competition ◽  
Firing Rates ◽  
Spike Time Dependent Plasticity ◽  
Population Rate ◽  
Self Organizing

It has been a matter of debate how firing rates or spatiotemporal spike patterns carry information in the brain. Recent experimental and theoretical work in part showed that these codes, especially a population rate code and a synchronous code, can be dually used in a single architecture. However, we are not yet able to relate the role of firing rates and synchrony to the spatiotemporal structure of inputs and the architecture of neural networks. In this article, we examine how feedforward neural networks encode multiple input sources in the firing patterns. We apply spike-time-dependent plasticity as a fundamental mechanism to yield synaptic competition and the associated input filtering. We use the Fokker-Planck formalism to analyze the mechanism for synaptic competition in the case of multiple inputs, which underlies the formation of functional clusters in downstream layers in a self-organizing manner. Depending on the types of feedback coupling and shared connectivity, clusters are independently engaged in population rate coding or synchronous coding, or they interact to serve as input filters. Classes of dual codings and functional roles of spike-time-dependent plasticity are also discussed.

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