Artificial NeuroGlial Networks

2011 ◽  
pp. 167-171 ◽  
Author(s):  
Ana Belén Porto Pazos ◽  
Alberto Alvarellos González ◽  
Félix Montañés Pazos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Classification Problems ◽  
Multilayer Networks ◽  
Process Information ◽  
Feed Forward ◽  
Complex Problems ◽  
Artificial Neural ◽  
The Brain

More than 50 years ago connectionist systems (CSs) were created with the purpose to process information in the computers like the human brain (McCulloch & Pitts, 1943). Since that time these systems have advanced considerably and nowadays they allow us to resolve complex problems in many disciplines (classification, clustering, regression, etc.). But this advance is not enough. There are still a lot of limitations when these systems are used (Dorado, 1999). Mostly the improvements were obtained following two different ways. Many researchers have preferred the construction of artificial neural networks (ANNs) based in mathematic models with diverse equations which lead its functioning (Cortes & Vapnik, 1995; Haykin, 1999). Otherwise other researchers have pretended the most possibly to make alike these systems to human brain (Rabuñal, 1999; Porto, 2004). The systems included in this article have emerged following the second way of investigation. CSs which pretend to imitate the neuroglial nets of the brain are introduced. These systems are named Artificial NeuroGlial Networks (ANGNs) (Porto, 2004). These CSs are not only made of neuron, but also from elements which imitate glial neurons named astrocytes (Araque, 1999). These systems, which have hybrid training, have demonstrated efficacy when resolving classification problems with totally connected feed-forward multilayer networks, without backpropagation and lateral connections.

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

Proceedings ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. 46
Author(s):  
Francisco Cedron ◽  
Sara Alvarez-Gonzalez ◽  
Alejandro Pazos ◽  
Ana B. Porto-Pazos
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Glial Cell ◽  
Process Information ◽  
Connectionist System ◽  
Artificial Neural ◽  
The Brain

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.

Recent Methodology in Connectionist Systems

2011 ◽  
pp. 94-106
Author(s):  
Ana B. Porto Pazos ◽  
Alberto Alvarellos González ◽  
Alejandro Pazos Sierra
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Glial Cells ◽  
Brain Networks ◽  
Classification Problems ◽  
Biological Phenomena ◽  
Artificial Neurons ◽  
Artificial Neural ◽  
The Brain

The Artificial NeuroGlial Networks, which try to imitate the neuroglial brain networks, appeared in order to process the information by means of artificial systems based on biological phenomena. They are not only made of artificial neurons, like the artificial neural networks, but also they are made of elements which try to imitate glial cells. An important glial role related with the processing of the brain information has been recently discovered but, as the functioning of the biological neuroglial networks is not exactly known, it is necessary to test several and different possibilities for creating Artificial NeuroGlial Networks. This chapter shows the functioning methodology of the Artificial NeuroGlial Networks and the application of a possible implementation of artificial glia to classification problems.

scholarly journals Encoding of speech in convolutional layers and the brain stem based on language experience

2022 ◽  
Author(s):  
Gasper Begus ◽  
Alan Zhou ◽  
Christina Zhao
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Human Brain ◽  
Brain Stem ◽  
Acoustic Property ◽  
Spanish Speakers ◽  
Peak Latency ◽  
Convolutional Networks ◽  
Artificial Neural ◽  
The Brain

Comparing artificial neural networks (ANNs) with outputs of brain imaging techniques has recently seen substantial advances in (computer) vision and text-based language models. Here, we propose a framework to compare biological and artificial neural computations of spoken language representations and propose several new challenges to this paradigm. Using a technique proposed by Begus and Zhou (2021b), we can analyze encoding of any acoustic property in intermediate convolutional layers of an artificial neural network. This allows us to test similarities in speech encoding between the brain and artificial neural networks in a way that is more interpretable than the majority of existing proposals that focus on correlations and supervised models. We introduce fully unsupervised deep generative models (the Generative Adversarial Network architecture) trained on raw speech to the brain-and-ANN-comparison paradigm, which enable testing of both the production and perception principles in human speech. We present a framework that parallels electrophysiological experiments measuring complex Auditory Brainstem Response (cABR) in human brain with intermediate layers in deep convolutional networks. We compared peak latency in cABR relative to the stimulus in the brain stem experiment, and in intermediate convolutional layers relative to the input/output in deep convolutional networks. We also examined and compared the effect of prior language exposure on the peak latency in cABR, and in intermediate convolutional layers of a phonetic property. Specifically, the phonetic property (i.e., VOT =10 ms) is perceived differently by English vs. Spanish speakers as voiced (e.g. [ba]) vs voiceless (e.g. [pa]). Critically, the cABR peak latency to the VOT phonetic property is different between English and Spanish speakers, and peak latency in intermediate convolutional layers is different between English-trained and Spanish-trained computational models. Substantial similarities in peak latency encoding between the human brain and intermediate convolutional networks emerge based on results from eight trained networks (including a replication experiment). The proposed technique can be used to compare encoding between the human brain and intermediate convolutional layers for any acoustic property.

scholarly journals A Novel Multimean Particle Swarm Optimization Algorithm for Nonlinear Continuous Optimization: Application to Feed-Forward Neural Network Training

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Mehmet Hacibeyoglu ◽  
Mohammed H. Ibrahim
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Artificial Neural Networks ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Swarm Optimization ◽  
Feed Forward ◽  
Artificial Neural

Multilayer feed-forward artificial neural networks are one of the most frequently used data mining methods for classification, recognition, and prediction problems. The classification accuracy of a multilayer feed-forward artificial neural networks is proportional to training. A well-trained multilayer feed-forward artificial neural networks can predict the class value of an unseen sample correctly if provided with the optimum weights. Determining the optimum weights is a nonlinear continuous optimization problem that can be solved with metaheuristic algorithms. In this paper, we propose a novel multimean particle swarm optimization algorithm for multilayer feed-forward artificial neural networks training. The proposed multimean particle swarm optimization algorithm searches the solution space more efficiently with multiple swarms and finds better solutions than particle swarm optimization. To evaluate the performance of the proposed multimean particle swarm optimization algorithm, experiments are conducted on ten benchmark datasets from the UCI repository and the obtained results are compared to the results of particle swarm optimization and other previous research in the literature. The analysis of the results demonstrated that the proposed multimean particle swarm optimization algorithm performed well and it can be adopted as a novel algorithm for multilayer feed-forward artificial neural networks training.

scholarly journals PREDICTION OF FINANCIAL DISTRESS: CASE OF MINING ENTERPRISES IN CZECH REPUBLIC

2020 ◽  
Vol 15 (1) ◽  
pp. 1-14
Author(s):  
Zuzana Rowland ◽  
Alla Kasych ◽  
Petr Suler
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Czech Republic ◽  
Financial Stability ◽  
Grey Zone ◽  
Financial Health ◽  
Classification Problems ◽  
Mining Companies ◽  
Artificial Neural

The ability to predict a company's financial health is a challenge for many researchers and scientists. It is also a distracting topic, as many other new approaches to financial health predictions have emerged in recent years. In this paper, we focused on identifying the financial health of mining companies in the Czech Republic. We chose the neural network method because, based on various instances of related research, neural networks represent a more reliable financial forecast than mathematical-statistical methods such as discriminant analysis and logistic regression. The concept of a neural network emerged with the first artificial neural networks, inspired by biological systems. The existence of prediction and classification problems directly predetermines artificial neural networks with respect to a given issue. We used the Amadeus database for processing, including financial indicators, SPSS, and Visual Gene Developer software. In total, we analyzed sixty-four mining companies. Complete data on financial stability were available for fifty-three companies, which we explored, and based on these results, identified financial situations for the other thirteen. Based on the available information, we processed a neural network and regression analysis. We managed to classify thirteen companies as solvent, insolvent, and in the grey zone, with the help of prediction.

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

2021 ◽  
pp. 1-31
Author(s):  
Pankaj Dadheech ◽  
Ankit Kumar ◽  
Vijander Singh ◽  
Linesh Raja ◽  
Ramesh C. Poonia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Neural Structure ◽  
Modern Agriculture ◽  
Different Types ◽  
Pest Detection ◽  
Real Time Applications ◽  
Artificial Neural ◽  
And Control ◽  
The Brain

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.

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