scholarly journals Rhesus monkey (Macaca mulatta) cool sensitivity measured by a signal detection method

1976 ◽  
Vol 19 (3) ◽  
pp. 246-251 ◽  
Author(s):  
Helen H. Molinari ◽  
Andrew J. Rózsa ◽  
Dan R. Kenshalo
Keyword(s):  
Rhesus Monkey ◽  
Signal Detection ◽  
Macaca Mulatta ◽  
Detection Method

scholarly journals Inosine induces acute hyperuricaemia in rhesus monkey (Macaca mulatta) as a potential disease animal model

2021 ◽  
Vol 59 (1) ◽  
pp. 175-182
Author(s):  
Dong-hong Tang ◽  
Chen-yun Wang ◽  
Xi Huang ◽  
Hong-kun Yi ◽  
Zhe-li Li ◽  
...  
Keyword(s):  
Animal Model ◽  
Rhesus Monkey ◽  
Macaca Mulatta

Accuracy of the regulation of caloric ingestion in the rhesus monkey

1978 ◽  
Vol 235 (1) ◽  
pp. R29-R34 ◽  
Author(s):  
P. R. McHugh ◽  
T. H. Moran
Keyword(s):  
Rhesus Monkey ◽  
Macaca Mulatta ◽  
Caloric Intake ◽  
Feeding Period ◽  
Random Fashion ◽  
Series Of Experiments

In seven male monkeys, Macaca mulatta, the infusion of nutrients into the stomach just prior to or 20 h before a 4-h feeding period reduced the feeding by an amount comparable to the calories infused. Pure carbohydrates, fat, protein, and mixtures were employed as infusions and given in a random fashion over a caloric range of 75-300 kcal. In a second series of experiments, monkeys were partially fasted on 1 day and in this way deprived of 75, 150, 300, or 450 kcal. On successive days, they overate to compensate for this deprivation. The smaller deprivations (75 and 150 kcal) were corrected on the first recovery day. The 300-kcal deprivation required 2 days to be corrected while the 450-kcal deficit was only partially restored. These experiments demonstrate the capacities of the monkey to respond with precision to caloric supply and deprivation so as to maintain a constant caloric intake.

scholarly journals Spectral Convolution Feature-Based SPD Matrix Representation for Signal Detection Using a Deep Neural Network

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 949
Author(s):  
Jiangyi Wang ◽  
Min Liu ◽  
Xinwu Zeng ◽  
Xiaoqiang Hua
Keyword(s):  
Neural Network ◽  
Signal Detection ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Detection Method ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Data Sets ◽  
Feature Maps ◽  
Simulated Data Sets

Convolutional neural networks have powerful performances in many visual tasks because of their hierarchical structures and powerful feature extraction capabilities. SPD (symmetric positive definition) matrix is paid attention to in visual classification, because it has excellent ability to learn proper statistical representation and distinguish samples with different information. In this paper, a deep neural network signal detection method based on spectral convolution features is proposed. In this method, local features extracted from convolutional neural network are used to construct the SPD matrix, and a deep learning algorithm for the SPD matrix is used to detect target signals. Feature maps extracted by two kinds of convolutional neural network models are applied in this study. Based on this method, signal detection has become a binary classification problem of signals in samples. In order to prove the availability and superiority of this method, simulated and semi-physical simulated data sets are used. The results show that, under low SCR (signal-to-clutter ratio), compared with the spectral signal detection method based on the deep neural network, this method can obtain a gain of 0.5–2 dB on simulated data sets and semi-physical simulated data sets.

The role of vocal tract filtering in identity cueing in rhesus monkey (Macaca mulatta) vocalizations

1998 ◽  
Vol 103 (1) ◽  
pp. 602-614 ◽  
Author(s):  
Drew Rendall ◽  
Michael J. Owren ◽  
Peter S. Rodman
Keyword(s):  
Rhesus Monkey ◽  
Macaca Mulatta ◽  
Vocal Tract
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