Single-Neuron Analysis Using CE Combined with MALDI MS and Radionuclide Detection

2002 ◽  
Vol 74 (3) ◽  
pp. 497-503 ◽  
Author(s):  
Jason S. Page ◽  
Stanislav S. Rubakhin ◽  
Jonathan V. Sweedler
Keyword(s):  
Single Neuron ◽  
Maldi Ms ◽  
Radionuclide Detection

Characteristic MALDI-MS lipid profiles of Gir, Holstein and crossbred (Gir x Holstein) oocytes recovered by ovum pick-up

2021 ◽  
Vol 243 ◽  
pp. 104380
Author(s):  
Thamiris Vieira Marsico ◽  
José Nélio de Sousa Sales ◽  
Christina Ramires Ferreira ◽  
Mateus José Sudano ◽  
João Henrique Moreira Viana ◽  
...  
Keyword(s):  
Lipid Profiles ◽  
Maldi Ms

scholarly journals Deep neural networks using a single neuron: folded-in-time architecture using feedback-modulated delay loops

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Florian Stelzer ◽  
André Röhm ◽  
Raul Vicente ◽  
Ingo Fischer ◽  
Serhiy Yanchuk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Single Neuron ◽  
Deep Neural Networks ◽  
Back Propagation ◽  
Local Network ◽  
Multiple Time ◽  
Learning Tools ◽  
Back Propagation Algorithm

AbstractDeep neural networks are among the most widely applied machine learning tools showing outstanding performance in a broad range of tasks. We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops. This single-neuron deep neural network comprises only a single nonlinearity and appropriately adjusted modulations of the feedback signals. The network states emerge in time as a temporal unfolding of the neuron’s dynamics. By adjusting the feedback-modulation within the loops, we adapt the network’s connection weights. These connection weights are determined via a back-propagation algorithm, where both the delay-induced and local network connections must be taken into account. Our approach can fully represent standard Deep Neural Networks (DNN), encompasses sparse DNNs, and extends the DNN concept toward dynamical systems implementations. The new method, which we call Folded-in-time DNN (Fit-DNN), exhibits promising performance in a set of benchmark tasks.

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