Neural computation, architecture, and evolution

Neural Computation ◽

Artificial Neural ◽

Biological neural computation relies a great deal on architecture, which constrains the types of content that can be processed by distinct modules in the brain. Though artificial neural networks are useful tools and give insight, they cannot be relied upon yet to give definitive answers to problems in cognition. Knowledge re-use may be driven more by architectural inheritance than by epistemological drives.

Advances in Environmental Engineering and Green Technologies - Smart Agricultural Services Using Deep Learning, Big Data, and IoT ◽

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

10.4018/978-1-7998-5003-8.ch001 ◽

2021 ◽

pp. 1-31

Author(s):

Pankaj Dadheech ◽

Ankit Kumar ◽

Vijander Singh ◽

Linesh Raja ◽

Ramesh C. Poonia

Keyword(s):

Neural Networks ◽

Neural Structure ◽

Modern Agriculture ◽

Different Types ◽

Pest Detection ◽

Real Time Applications ◽

Artificial Neural ◽

And Control ◽

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

Use of Multiple Astrocytic Configurations within an Artificial Neuro-Astrocytic Network

Proceedings ◽

10.3390/proceedings2019021046 ◽

2019 ◽

Vol 21 (1) ◽

pp. 46

Author(s):

Francisco Cedron ◽

Sara Alvarez-Gonzalez ◽

Alejandro Pazos ◽

Ana B. Porto-Pazos

Keyword(s):

Neural Networks ◽

Glial Cell ◽

Process Information ◽

Connectionist System ◽

Artificial Neural ◽

The artificial neural networks used in a multitude of fields are achieving good results. However, these systems are inspired in the vision of classical neuroscience where neurons are the only elements that process information in the brain. Advances in neuroscience have shown that there is a type of glial cell called astrocytes that collaborate with neurons to process information. In this work, a connectionist system formed by neurons and artificial astrocytes is presented. The astrocytes can have different configurations to achieve a biologically more realistic behaviour. This work indicates that the use of different artificial astrocytes behaviours is beneficial.

Using chaotic artificial neural networks to model memory in the brain

Communications in Nonlinear Science and Numerical Simulation ◽

10.1016/j.cnsns.2016.08.025 ◽

2017 ◽

Vol 44 ◽

pp. 449-459 ◽

Cited By ~ 36

Author(s):

Zainab Aram ◽

Sajad Jafari ◽

Jun Ma ◽

Julien C. Sprott ◽

Sareh Zendehrouh ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural ◽

Artificial Intelligence to Analyze the Cortical Thickness Through Age

Frontiers in Artificial Intelligence ◽

10.3389/frai.2021.549255 ◽

2021 ◽

Vol 4 ◽

Author(s):

Sergio Ledesma ◽

Mario-Alberto Ibarra-Manzano ◽

Dora-Luz Almanza-Ojeda ◽

Pascal Fallavollita ◽

Jason Steffener

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Left Hemisphere ◽

Cortical Thickness ◽

Right Hemisphere ◽

Unseen Data ◽

Artificial Neural ◽

The Right ◽

In this study, Artificial Intelligence was used to analyze a dataset containing the cortical thickness from 1,100 healthy individuals. This dataset had the cortical thickness from 31 regions in the left hemisphere of the brain as well as from 31 regions in the right hemisphere. Then, 62 artificial neural networks were trained and validated to estimate the number of neurons in the hidden layer. These neural networks were used to create a model for the cortical thickness through age for each region in the brain. Using the artificial neural networks and kernels with seven points, numerical differentiation was used to compute the derivative of the cortical thickness with respect to age. The derivative was computed to estimate the cortical thickness speed. Finally, color bands were created for each region in the brain to identify a positive derivative, that is, a part of life with an increase in cortical thickness. Likewise, the color bands were used to identify a negative derivative, that is, a lifetime period with a cortical thickness reduction. Regions of the brain with similar derivatives were organized and displayed in clusters. Computer simulations showed that some regions exhibit abrupt changes in cortical thickness at specific periods of life. The simulations also illustrated that some regions in the left hemisphere do not follow the pattern of the same region in the right hemisphere. Finally, it was concluded that each region in the brain must be dynamically modeled. One advantage of using artificial neural networks is that they can learn and model non-linear and complex relationships. Also, artificial neural networks are immune to noise in the samples and can handle unseen data. That is, the models based on artificial neural networks can predict the behavior of samples that were not used for training. Furthermore, several studies have shown that artificial neural networks are capable of deriving information from imprecise data. Because of these advantages, the results obtained in this study by the artificial neural networks provide valuable information to analyze and model the cortical thickness.

Intelligent Traffic Sign Classifiers

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch141 ◽

2011 ◽

pp. 956-962

Author(s):

Raúl Vicen Bueno ◽

Elena Torijano Gordo ◽

Antonio García González ◽

Manuel Rosa Zurera ◽

Roberto Gil Pita

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Main Part ◽

Point Of View ◽

Traffic Sign ◽

Traffic Signs ◽

Artificial Neural ◽

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.

Intelligent Radar Detectors

Encyclopedia of Artificial Intelligence ◽

10.4018/978-1-59904-849-9.ch137 ◽

2011 ◽

pp. 933-939

Author(s):

Raúl Vicen Bueno ◽

Manuel Rosa Zurera ◽

María Pilar Jarabo Amores ◽

Roberto Gil Pita ◽

David de la Mata Moya

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Main Part ◽

Point Of View ◽

Communications Systems ◽

Reflected Signal ◽

Artificial Neural ◽

The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.

Artificial Neural Networks for Prediction

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch011 ◽

2011 ◽

pp. 54-58

Author(s):

Rafael Marti

Keyword(s):

Neural Networks ◽

Intelligent Systems ◽

Intelligent System ◽

Human Capabilities ◽

Design And Implementation ◽

Starting Point ◽

Input Signals ◽

Artificial Neural ◽

The design and implementation of intelligent systems with human capabilities is the starting point to design Artificial Neural Networks (ANNs). The original idea takes after neuroscience theory on how neurons in the human brain cooperate to learn from a set of input signals to produce an answer. Because the power of the brain comes from the number of neurons and the multiple connections between them, the basic idea is that connecting a large number of simple elements in a specific way can form an intelligent system.

OPL034 Role of artificial neural networks in the analysis of 1H magnetic resonance spectroscopic data in the diagnostic workup of solitary ring enhancing lesions of the brain

Journal of the Neurological Sciences ◽

10.1016/s0022-510x(05)80212-6 ◽

2005 ◽

Vol 238 ◽

pp. S53-S54

Keyword(s):

Neural Networks ◽

Magnetic Resonance ◽

Spectroscopic Data ◽

Diagnostic Workup ◽

Artificial Neural ◽

Research on the Development and Applications of Artificial Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.556-562.6011 ◽

2014 ◽

Vol 556-562 ◽

pp. 6011-6014 ◽

Cited By ~ 1

Author(s):

Xiao Guang Li

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Intelligent Control ◽

Computational Models ◽

Bayesian Probability ◽

Control Techniques ◽

Central Nervous Systems ◽

Artificial Neural ◽

Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.