scholarly journals A Dynamic Shift of Neural Network Activity before and after Learning-set Formation

2004 ◽  
Vol 15 (6) ◽  
pp. 796-801 ◽  
Author(s):  
Chihiro Yokoyama ◽  
Hideo Tsukada ◽  
Yasuyoshi Watanabe ◽  
Hirotaka Onoe
Keyword(s):  
Neural Network ◽  
Network Activity ◽  
Before And After ◽  
Learning Set ◽  
Dynamic Shift

scholarly journals Vagal stimulation targets select populations of intrinsic cardiac neurons to control neurally induced atrial fibrillation

2016 ◽  
Vol 311 (5) ◽  
pp. H1311-H1320 ◽  
Author(s):  
Siamak Salavatian ◽  
Eric Beaumont ◽  
Jean-Philippe Longpré ◽  
J. Andrew Armour ◽  
Alain Vinet ◽  
...  
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Nerve Stimulation ◽  
Microelectrode Array ◽  
Vagal Stimulation ◽  
Network Activity ◽  
Right Atrial ◽  
Vessel Occlusion ◽  
Before And After ◽  
The Right

Mediastinal nerve stimulation (MNS) reproducibly evokes atrial fibrillation (AF) by excessive and heterogeneous activation of intrinsic cardiac (IC) neurons. This study evaluated whether preemptive vagus nerve stimulation (VNS) impacts MNS-induced evoked changes in IC neural network activity to thereby alter susceptibility to AF. IC neuronal activity in the right atrial ganglionated plexus was directly recorded in anesthetized canines ( n = 8) using a linear microelectrode array concomitant with right atrial electrical activity in response to: 1) epicardial touch or great vessel occlusion vs. 2) stellate or vagal stimulation. From these stressors, post hoc analysis (based on the Skellam distribution) defined IC neurons so recorded as afferent, efferent, or convergent (afferent and efferent inputs) local circuit neurons (LCN). The capacity of right-sided MNS to modify IC activity in the induction of AF was determined before and after preemptive right (RCV)- vs. left (LCV)-sided VNS (15 Hz, 500 μs; 1.2× bradycardia threshold). Neuronal ( n = 89) activity at baseline (0.11 ± 0.29 Hz) increased during MNS-induced AF (0.51 ± 1.30 Hz; P < 0.001). Convergent LCNs were preferentially activated by MNS. Preemptive RCV reduced MNS-induced changes in LCN activity (by 70%) while mitigating MNS-induced AF (by 75%). Preemptive LCV reduced LCN activity by 60% while mitigating AF potential by 40%. IC neuronal synchrony increased during neurally induced AF, a local neural network response mitigated by preemptive VNS. These antiarrhythmic effects persisted post-VNS for, on average, 26 min. In conclusion, VNS preferentially targets convergent LCNs and their interactive coherence to mitigate the potential for neurally induced AF. The antiarrhythmic properties imposed by VNS exhibit memory.

Neural network activity and neurological soft signs in healthy adults

2015 ◽  
Vol 278 ◽  
pp. 514-519 ◽  
Author(s):  
Philipp A. Thomann ◽  
Dusan Hirjak ◽  
Katharina M. Kubera ◽  
Bram Stieltjes ◽  
Robert C. Wolf
Keyword(s):  
Neural Network ◽  
Healthy Adults ◽  
Network Activity ◽  
Neurological Soft Signs

Beta and gamma synchronous oscillations in neural network activity in mice-induced by food deprivation

2019 ◽  
Vol 709 ◽  
pp. 134398
Author(s):  
Nifareeda Samerphob ◽  
Acharaporn Issuriya ◽  
Dania Cheaha ◽  
Surapong Chatpun ◽  
Ole Jensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Food Deprivation ◽  
Network Activity ◽  
Synchronous Oscillations

Distribution of VTA Glutamate and Dopamine Terminals, and their Significance in CA1 Neural Network Activity

Neuroscience ◽  
2020 ◽  
Vol 446 ◽  
pp. 171-198 ◽  
Author(s):  
Philip A. Adeniyi ◽  
Amita Shrestha ◽  
Olalekan M. Ogundele
Keyword(s):  
Neural Network ◽  
Network Activity

Statistical Methods and Artificial Neural Networks Techniques in Electromyography

2012 ◽  
Vol 1 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Ahmad Taher Azar ◽  
Valentina E. Balas
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Muscle Activity ◽  
Masseter Muscle ◽  
Discrete Wavelet ◽  
Neural Network Classifier ◽  
Denture Base ◽  
Feature Vectors ◽  
Significant Difference ◽  
Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

Bursting as a source for predictability in biological neural network activity

1997 ◽  
Vol 110 (3-4) ◽  
pp. 323-331 ◽  
Author(s):  
L. Menendez de la Prida ◽  
N. Stollenwerk ◽  
J.V. Sanchez-Andres
Keyword(s):  
Neural Network ◽  
Network Activity ◽  
Biological Neural Network

scholarly journals Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery

2019 ◽  
Vol 11 (21) ◽  
pp. 2492 ◽  
Author(s):  
Bo Peng ◽  
Zonglin Meng ◽  
Qunying Huang ◽  
Caixia Wang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Real Time ◽  
Spectral Reflectance ◽  
Urban Areas ◽  
Flood Mapping ◽  
Urban Flooding ◽  
Urban Flood ◽  
Before And After ◽  
Flood Maps

Urban flooding is a major natural disaster that poses a serious threat to the urban environment. It is highly demanded that the flood extent can be mapped in near real-time for disaster rescue and relief missions, reconstruction efforts, and financial loss evaluation. Many efforts have been taken to identify the flooding zones with remote sensing data and image processing techniques. Unfortunately, the near real-time production of accurate flood maps over impacted urban areas has not been well investigated due to three major issues. (1) Satellite imagery with high spatial resolution over urban areas usually has nonhomogeneous background due to different types of objects such as buildings, moving vehicles, and road networks. As such, classical machine learning approaches hardly can model the spatial relationship between sample pixels in the flooding area. (2) Handcrafted features associated with the data are usually required as input for conventional flood mapping models, which may not be able to fully utilize the underlying patterns of a large number of available data. (3) High-resolution optical imagery often has varied pixel digital numbers (DNs) for the same ground objects as a result of highly inconsistent illumination conditions during a flood. Accordingly, traditional methods of flood mapping have major limitations in generalization based on testing data. To address the aforementioned issues in urban flood mapping, we developed a patch similarity convolutional neural network (PSNet) using satellite multispectral surface reflectance imagery before and after flooding with a spatial resolution of 3 meters. We used spectral reflectance instead of raw pixel DNs so that the influence of inconsistent illumination caused by varied weather conditions at the time of data collection can be greatly reduced. Such consistent spectral reflectance data also enhance the generalization capability of the proposed model. Experiments on the high resolution imagery before and after the urban flooding events (i.e., the 2017 Hurricane Harvey and the 2018 Hurricane Florence) showed that the developed PSNet can produce urban flood maps with consistently high precision, recall, F1 score, and overall accuracy compared with baseline classification models including support vector machine, decision tree, random forest, and AdaBoost, which were often poor in either precision or recall. The study paves the way to fuse bi-temporal remote sensing images for near real-time precision damage mapping associated with other types of natural hazards (e.g., wildfires and earthquakes).

scholarly journals Carpets Color and Pattern Detection Based on Their Images †

Proceedings ◽  
2019 ◽  
Vol 33 (1) ◽  
pp. 28
Author(s):  
Sayedeh Hosseini ◽  
Ali Mohhamad-Djafari ◽  
Adel Mohammadpour ◽  
Sobhan Mohammadpour ◽  
Mohammad Nadi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Previous Experiment ◽  
Pattern Detection ◽  
Detection Algorithms ◽  
Color Detection ◽  
Human Counterpart ◽  
Learning Set

In these days of fast-paced business, accurate automatic color or pattern detection is a necessity for carpet retailers. Many well-known color detection algorithms have many shortcomings. Apart from the color itself, neighboring colors, style, and pattern also affects how humans perceive color. Most if not all, color detection algorithms do not take this into account. Furthermore, the algorithm needed should be invariant to changes in brightness, size, and contrast of the image. In a previous experiment, the accuracy of the algorithm was half of the human counterpart. Therefore, we propose a supervised approach to reduce detection errors. We used more than 37,000 images from a retailer’s database as the learning set to train a Convolutional Neural Network (CNN, or ConvNet) architecture.

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