scholarly journals Cortical network models of impulse firing in the resting and active states predict cortical energetics

2015 ◽  
Vol 112 (13) ◽  
pp. 4134-4139 ◽  
Author(s):  
Maxwell R. Bennett ◽  
Les Farnell ◽  
William G. Gibson ◽  
Jim Lagopoulos
Keyword(s):  
Resting State ◽  
Network Models ◽  
Quantitative Relationship ◽  
Nrem Sleep ◽  
Inhibitory Neurons ◽  
Neural Network Models ◽  
Impulse Frequency ◽  
State Frequency ◽  
The One ◽  
Cortical Models

Measurements of the cortical metabolic rate of glucose oxidation [CMRglc(ox)] have provided a number of interesting and, in some cases, surprising observations. One is the decline in CMRglc(ox) during anesthesia and non-rapid eye movement (NREM) sleep, and another, the inverse relationship between the resting-state CMRglc(ox) and the transient following input from the thalamus. The recent establishment of a quantitative relationship between synaptic and action potential activity on the one hand and CMRglc(ox) on the other allows neural network models of such activity to probe for possible mechanistic explanations of these phenomena. We have carried out such investigations using cortical models consisting of networks of modules with excitatory and inhibitory neurons, each receiving excitatory inputs from outside the network in addition to intermodular connections. Modules may be taken as regions of cortical interest, the inputs from outside the network as arising from the thalamus, and the intermodular connections as long associational fibers. The model shows that the impulse frequency of different modules can differ from each other by less than 10%, consistent with the relatively uniform CMRglc(ox) observed across different regions of cortex. The model also shows that, if correlations of the average impulse rate between different modules decreases, there is a concomitant decrease in the average impulse rate in the modules, consistent with the observed drop in CMRglc(ox) in NREM sleep and under anesthesia. The model also explains why a transient thalamic input to sensory cortex gives rise to responses with amplitudes inversely dependent on the resting-state frequency, and therefore resting-state CMRglc(ox).

Neural Network Models

2016 ◽  
pp. 368-395
Author(s):  
Eliano Pessa
Keyword(s):  
Neural Network ◽  
Statistical Physics ◽  
Optimization Theory ◽  
Network Models ◽  
Cooperative Interaction ◽  
New Paradigm ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
The One

The Artificial Neural Network (ANN) models gained a wide popularity owing to a number of claimed advantages such as biological plausibility, tolerance with respect to errors or noise in the input data, learning ability allowing an adaptability to environmental constraints. Notwithstanding the fact that most of these advantages are not typical only of ANNs, engineers, psychologists and neuroscientists made an extended use of ANN models in a large number of scientific investigations. In most cases, however, these models have been introduced in order to provide optimization tools more useful than the ones commonly used by traditional Optimization Theory. Unfortunately, just the successful performance of ANN models in optimization tasks produced a widespread neglect of the true – and important – objectives pursued by the first promoters of these models. These objectives can be shortly summarized by the manifesto of connectionist psychology, stating that mental processes are nothing but macroscopic phenomena, emergent from the cooperative interaction of a large number of microscopic knowledge units. This statement – wholly in line with the goal of statistical mechanics – can be readily extended to other processes, beyond the mental ones, including social, economic, and, in general, organizational ones. Therefore this chapter has been designed in order to answer a number of related questions, such as: are the ANN models able to grant for the occurrence of this sort of emergence? How can the occurrence of this emergence be empirically detected? How can the emergence produced by ANN models be controlled? In which sense the ANN emergence could offer a new paradigm for the explanation of macroscopic phenomena? Answering these questions induces to focus the chapter on less popular ANNs, such as the recurrent ones, while neglecting more popular models, such as perceptrons, and on less used units, such as spiking neurons, rather than on McCulloch-Pitts neurons. Moreover, the chapter must mention a number of strategies of emergence detection, useful for researchers performing computer simulations of ANN behaviours. Among these strategies it is possible to quote the reduction of ANN models to continuous models, such as the neural field models or the neural mass models, the recourse to the methods of Network Theory and the employment of techniques borrowed by Statistical Physics, like the one based on the Renormalization Group. Of course, owing to space (and mathematical expertise) requirements, most mathematical details of the proposed arguments are neglected, and, to gain more information, the reader is deferred to the quoted literature.

scholarly journals The One-Stage Detector Algorithm Based on Background Prediction and Group Normalization for Vehicle Detection

2020 ◽  
Vol 10 (17) ◽  
pp. 5883
Author(s):  
Fei Lu ◽  
Fei Xie ◽  
Shibin Shen ◽  
Jiquan Yang ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Network Models ◽  
Vehicle Detection ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Detector Performance ◽  
One Stage ◽  
Complex Scenes ◽  
The One

Vehicle detection in intelligent transportation systems (ITS) is a very important and challenging task in traffic monitoring. The difficulty of this task is to accurately locate and classify relatively small vehicles in complex scenes. To solve these problems, this paper proposes a modified one-stage detector based on background prediction and group normalization to realize real-time and accurate detection of traffic vehicles. The one-stage detector firstly adds a module to adjust the width and height of anchors and predict the target background, which avoids the problem of the target vehicle missing detection or wrong detection due to the influence of the complicated environments. Then, group normalization and the loss function based on weight attenuation can improve the one-stage detector performance in the training process. The experimental results on traffic monitoring datasets indicate that the improved one-stage detector is superior to the other neural network models in terms of precision at 95.78%.

scholarly journals Using artificial neural network models for the prediction of thrust force and torque in drilling operation of Al7075

2018 ◽  
Vol 178 ◽  
pp. 01008
Author(s):  
Panagiotis Kyratsis ◽  
Nikolaos Efkolidis ◽  
Daniel Ghiculescu ◽  
Konstantinos Kakoulis
Keyword(s):  
Thrust Force ◽  
Network Models ◽  
Cutting Parameters ◽  
Drilling Process ◽  
Neural Network Models ◽  
Cutting Velocity ◽  
Artificial Neural ◽  
Hidden Layer ◽  
The One ◽  
Tool Diameter

This study investigates the thrust force (Fz) and torque (Mz) in a drilling process of an Al7075 workpiece using solid carbide tools (Kennametal KC7325), depending on the effects of crucial cutting parameters such as cutting velocity, feed rate and tool diameter of 10mm, 12mm and 14mm. Artificial neural networks (ANN) methodology is used in order to acquire mathematical models for both the thrust force (Fz) and torque (Mz) related to the drilling process. The ANN results showed that the best prediction topology of the network for the thrust force was the one with five neurons in the hidden layer, while for the case of Mz the best network topology for the prediction of the experimental values was the one with six neurons in the hidden layer. Based on the results acquired, the ANN models achieved accuracy of 1,96% and 1,95% for both the thrust force and torque measured, while the R coefficient for the prediction model of the thrust force is 0.99976 and 00.99981 for the torque. As a result they can be considered as very accurate and appropriate for their prediction.

Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction

2019 ◽  
Vol 31 (11) ◽  
pp. 2252-2265
Author(s):  
Felix Weissenberger ◽  
Marcelo Matheus Gauy ◽  
Xun Zou ◽  
Angelika Steger
Keyword(s):  
Neural Network ◽  
Nonlinear Interaction ◽  
Network Models ◽  
Inhibitory Neurons ◽  
Inhibitory Synapses ◽  
Synaptic Connections ◽  
Mutual Inhibition ◽  
Neural Network Models ◽  
Excitatory Neurons ◽  
Inhibitory Population

In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.

scholarly journals Mineral Exploation Using Neural Netowrks

2016 ◽  
Vol 15 (9) ◽  
pp. 7110-7116
Author(s):  
Aysar A. Abdulrahman
Keyword(s):  
Neural Network ◽  
Mineral Exploration ◽  
Network Models ◽  
Mining Operation ◽  
Visual Exploration ◽  
Neural Network Models ◽  
Java Programming ◽  
Som Algorithm ◽  
Geologic Model ◽  
The One

Establishing a new site for mining operation is required to find and develop a new source of minerals with precise characteristics such as location, depth, quality and thickness. Mineral exploration is a sequential process of informationgathering that assesses the mineral potential of given area. It starts with an idea of geologic model that identifies lands worthy of further exploration, and it’s the one of risky and costly investments for companies. In this paper, a new method for choosing a scientific map of exploration using neural network was introduced rather than an arbitrary map. The goal of this paper is to demonstrate the effectiveness of Self Organization Map (SOM) algorithm in visual exploration of physical geographic data. The SOM is one of the most popular neural network models, which provides a data visualizationtechnique which helps to understand high dimensional data by reducing the dimensions of data to maps. In the present paper, the algorithm is based on unsupervised learning, and Java programming codes is used to simulate the SOM algorithm.

Neural Network Models

2016 ◽  
pp. 100-127
Author(s):  
Eliano Pessa
Keyword(s):  
Neural Network ◽  
Statistical Physics ◽  
Optimization Theory ◽  
Network Models ◽  
Cooperative Interaction ◽  
New Paradigm ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural Network Ann ◽  
The One

The Artificial Neural Network (ANN) models gained a wide popularity owing to a number of claimed advantages such as biological plausibility, tolerance with respect to errors or noise in the input data, learning ability allowing an adaptability to environmental constraints. Notwithstanding the fact that most of these advantages are not typical only of ANNs, engineers, psychologists and neuroscientists made an extended use of ANN models in a large number of scientific investigations. In most cases, however, these models have been introduced in order to provide optimization tools more useful than the ones commonly used by traditional Optimization Theory. Unfortunately, just the successful performance of ANN models in optimization tasks produced a widespread neglect of the true – and important – objectives pursued by the first promoters of these models. These objectives can be shortly summarized by the manifesto of connectionist psychology, stating that mental processes are nothing but macroscopic phenomena, emergent from the cooperative interaction of a large number of microscopic knowledge units. This statement – wholly in line with the goal of statistical mechanics – can be readily extended to other processes, beyond the mental ones, including social, economic, and, in general, organizational ones. Therefore this chapter has been designed in order to answer a number of related questions, such as: are the ANN models able to grant for the occurrence of this sort of emergence? How can the occurrence of this emergence be empirically detected? How can the emergence produced by ANN models be controlled? In which sense the ANN emergence could offer a new paradigm for the explanation of macroscopic phenomena? Answering these questions induces to focus the chapter on less popular ANNs, such as the recurrent ones, while neglecting more popular models, such as perceptrons, and on less used units, such as spiking neurons, rather than on McCulloch-Pitts neurons. Moreover, the chapter must mention a number of strategies of emergence detection, useful for researchers performing computer simulations of ANN behaviours. Among these strategies it is possible to quote the reduction of ANN models to continuous models, such as the neural field models or the neural mass models, the recourse to the methods of Network Theory and the employment of techniques borrowed by Statistical Physics, like the one based on the Renormalization Group. Of course, owing to space (and mathematical expertise) requirements, most mathematical details of the proposed arguments are neglected, and, to gain more information, the reader is deferred to the quoted literature.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

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