scholarly journals Prediction of inverse relationship between compression phase duration and expulsive airflow during voluntary cough in humans by a joint neural network biomechanical computational model

2013 ◽  
Vol 27 (S1) ◽  
Author(s):  
Donald C Bolser ◽  
Teresa E Pitts ◽  
Russell O'Connor ◽  
Lauren S Segers ◽  
Christine M Sapienza ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Inverse Relationship ◽  
Phase Duration ◽  
Compression Phase ◽  
Voluntary Cough

How to Compare Two Quantities? A Computational Model of Flutter Discrimination

2007 ◽  
Vol 19 (3) ◽  
pp. 409-419 ◽  
Author(s):  
Tom Verguts
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Model ◽  
Neural Network Model ◽  
Assignment Problem ◽  
Time Window ◽  
Cognitive Domains ◽  
Supervised Training ◽  
Comparison Problem ◽  
Stimulus Time

A task that has been intensively studied at the neural level is f lutter discrimination. I argue that f lutter discrimination entails a combination of a temporal assignment problem and a quantity comparison problem, and propose a neural network model of how these problems are solved. The network combines unsupervised and one-layer supervised training. The unsupervised part clusters input features (stimulus + time window) and the supervised part categorizes the resulting clusters. After training, the model shows a good fit with both neural and behavioral properties. New predictions are outlined and links with other cognitive domains are pointed out.

scholarly journals UNORGANIZED AIR RELEASES WITH RADIOACTIVE AEROSOLS FROM THE NEW SAFE CONFINIMENT OF CHNPP INTO THE ENVIRONMENT

2021 ◽  
pp. 181-186
Author(s):  
P.G. Krukovskyi ◽  
Ye.V. Diadiushko ◽  
D.J. Skliarenko ◽  
I.S. Starovit
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Network Technology ◽  
Building Structures ◽  
Flow Rates ◽  
Radioactive Aerosols ◽  
Computational Performance ◽  
Lack Of Information ◽  
Chernobyl Npp ◽  
Neural Network Technology

The New Safe Confinement (NSC) of the Chernobyl NPP, which isolates the destroyed reactor and the “Shelter Object” from the environment, is not airtight, so the problem is the lack of information on the flow of unorganized air with radioactive aerosols outside the NSC. This work presents computational model of the hydraulic state of the NSC, which allows to determine these flow rates through the leaks in the shells and building structures under the walls of the NSC. In addition to the developed model, the NSC hydraulic state model, created by neural network technology, was tested, which showed similar results and much higher computational performance, which allows its use for analysis and prediction of NSC`s hydraulic state in real time.

The Cocktail Party Listener

1994 ◽  
Vol 23 (482) ◽  
Author(s):  
A. R. Kian Abolfazlian ◽  
Brian K. Karlsen
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computational Model ◽  
Spectral Data ◽  
Spatial Data ◽  
Speaker Identification ◽  
Cocktail Party ◽  
Human Ability

A complex computational model of the human ability to listen to certain signals in preference of others, also called the cocktail party phenomenon, is built on the basis of surveys into the relevant psychological, DSP, and neural network literature. This model is basically binaural and as such it makes use of both spectral data and spatial data in determining which speaker to listen to. The model uses two neural networks for filtering and speaker identification. Results from some experimentation with type and architecture of these networks are presented along with the results of the model. These results indicate that the model has a distinctive ability to focus on a particular speaker of choice.

scholarly journals Hey, look over there: Distraction effects on rapid sequence recall

2019 ◽  
Author(s):  
Daniel Miner ◽  
Christian Tetzlaff
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Everyday Life ◽  
Recurrent Neural Network ◽  
Distinct Effect ◽  
Rapid Sequence ◽  
Basic Understanding ◽  
Different Types ◽  
The Brain ◽  
Huge Variety

AbstractIn the course of everyday life, the brain must store and recall a huge variety of representations of stimuli which are presented in an ordered or sequential way. The processes by which the ordering of these various things is stored and recalled are moderately well understood. We use here a computational model of a cortex-like recurrent neural network adapted by a multitude of plasticity mechanisms. We first demonstrate the learning of a sequence. Then, we examine the influence of different types of distractors on the network dynamics during the recall of the encoded ordered information being ordered in a sequence. We are able to broadly arrive at two distinct effect-categories for distractors, arrive at a basic understanding of why this is so, and predict what distractors will fall into each category.

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