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On Correspondences between Feedforward Artificial Neural Networks on Finite Memory Automata and Classes of Primitive Recursive Functions

Mathematics, 2023
When realized on computational devices with finite quantities of memory, feedforward artificial neural networks and the functions they compute cease being abstract mathematical objects and turn into executable programs generating concrete computations ...
Vladimir A. Kulyukin
doaj   +1 more source

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks [PDF]

, 2018
Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence.
Abbott, Abraham, Abraham, Alexander, Allis, Alpaydin, Anderson, Andrea Soltoggio, Angeline, Arifovic, Arnold, Arnold, Ay, Bailey, Baldwin, Baxter, Baxter, Bear, Beer, Bengio, Bengio, Bentley, Best, Bienenstock, Birmingham, Blynel, Boers, Bourlard, Brown, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Bullinaria, Butz, Butz, Bäck, Cabessa, Carew, Carlson, Carver, Cervier, Chklovskii, Clark, Cliff, Clutton-Brock, Coleman, Cooper, Damasio, Darwin, Dawkins, Deary, Deng, de Vladar, Di Paolo, Di Paolo, Dobzhansky, Doidge, Downing, Downing, Doya, Doya, Draganski, Dudai, Durr, Edelman, Eiben, Ellefsen, Ellefsen, Eskridge, Fellous, Fernando, Fernando, Finnie, Floreano, Floreano, Floreano, Floreano, Floreano, Floreano, Fogel, Fontanari, Friston, Fujii, Funahashi, Gerstner, Gerstner, Goldberg, Goodfellow, Graves, Greve, Grossberg, Gruau, Gu, Gustafsson, Hansen, Happel, Harrington, Harris-Warrick, Harvey, Hasselmo, Hasselmo, Hasselmo, Hawkins, Hebb, Hensch, Hinton, Hinton, Hinton, Hochreiter, Hoinville, Holland, Holland, Hopkins, Hornby, Howard, Howard, Howard, Hull, Husbands, Izhikevich, Izhikevich, Jaeger, Jo, Kaas, Kandel, Kandel, Katz, Katz, Katz, Kenneth O. Stanley, Khan, Khan, Khan, Khan, Kirkpatrick, Kiyota, Klug, Kohonen, Kohonen, Kolb, Kolb, Kondo, Koutník, Koza, Krichmar, Krizhevsky, Kumaran, Kupfermann, Køppe, Lake, Lalejini, Lamprecht, Langton, Lanzi, LeCun, LeDoux, Lee, Lehman, Lehman, Lehman, Levy, Lüders, Lüders, Maass, Maniadakis, Marder, Marder, Markram, Mattiussi, Mayley, McClelland, McQuesten, Meng, Merzenich, Michalewicz, Michalski, Miconi, Miller, Miller, Millington, Monroe, Mouret, Murre, Niv, Nogueira, Nogueira, Nolfi, Nolfi, Nolfi, Nolfi, Norouzzadeh, Offerman, Oja, Oquab, Orchard, Ormrod, Pan, Pascual-Leone, Pavlov, Pehlevan, Pigliucci, Rauschecker, Rawal, Rescorla, Risi, Risi, Risi, Risi, Risi, Roberts, Robins, Roff, Rolls, Rumelhart, Russell, Russo, Sanchez, Schmidhuber, Schmidhuber, Schmidhuber, Schmidhuber, Schreiweis, Schroll, Schultz, Schultz, Schultz, Sebastian Risi, Silva, Silva, Silver, Sims, Sims, Sipper, Skinner, Skinner, Smith, Smith, Smith, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Soltoggio, Sporns, Staddon, Stanley, Stanley, Stanley, Stanley, Stanley, Steels, Stone, Suri, Suri, Sutton, Taylor, Tessier-Lavigne, Thorndike, Thrun, Thrun, Thrun, Tonelli, Tonelli, Urzelai, Varela, Venkatesh, Vitay, Wagner, Walters, Widrow, Willshaw, Yamauchi, Yao, Yao, Yoder, Young, Ziemke   +282 more
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Artificial Intelligence to Analyze the Cortical Thickness Through Age

Frontiers in Artificial Intelligence, 2021
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 ...
Sergio Ledesma, Sergio Ledesma, Mario-Alberto Ibarra-Manzano, Dora-Luz Almanza-Ojeda, Pascal Fallavollita, Jason Steffener   +5 more
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A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

Adsorption Science & Technology, 2022
The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a ...
Hilda Elizabeth Reynel-Ávila, Ismael Alejandro Aguayo-Villarreal, Lizbeth Liliana Diaz-Muñoz, Jaime Moreno-Pérez, Francisco Javier Sánchez-Ruiz, Cintia Karina Rojas-Mayorga, Didilia Ileana Mendoza-Castillo, Adrián Bonilla-Petriciolet   +7 more
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Estimation of soil properties by an artificial neural network

Magazine of Civil Engineering, 2022
Empirical dependencies are often used in various fields of geotechnics and civil engineering. The existing empirical formulas are mainly developed with the use of regression and multiple regression.
Ofrikhter Ian, Ponomaryov Andrey, Zakharov Alexander, Shenkman Roman   +3 more
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SIR: A New Wireless Sensor Network Routing Protocol Based on Artificial Intelligence [PDF]

, 2006
Currently, Wireless Sensor Networks (WSNs) are formed by hundreds of low energy and low cost micro-electro-mechanical systems. Routing and low power consumption have become important research issues to interconnect this kind of networks.
Barbancho Concejero, Antonio, Barbancho Concejero, Julio, León de Mora, Carlos, Molina Cantero, Francisco Javier   +3 more
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Artificial neural networks compared with Bayesian generalized linear regression for leaf rust resistance prediction in Arabica coffee [PDF]

Pesquisa Agropecuária Brasileira, 2017
: The objective of this work was to evaluate the use of artificial neural networks in comparison with Bayesian generalized linear regression to predict leaf rust resistance in Arabica coffee (Coffea arabica).
Gabi Nunes Silva, Moysés Nascimento, Isabela de Castro Sant’Anna, Cosme Damião Cruz, Eveline Teixeira Caixeta, Pedro Crescêncio Souza Carneiro, Renato Domiciano Silva Rosado, Kátia Nogueira Pestana, Dênia Pires de Almeida, Marciane da Silva Oliveira   +9 more
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Mathematical model for the evaluation of risk of emergency situations at a dangerous technical object based on artificial neural networks

SHS Web of Conferences, 2018
In the work performed adaptation of artificial neural networks in modern security systems potentially dangerous technical objects — high-rise buildings as tools for assessing and forecasting in management decision.
Peganov Nikolay, Tumanov Aleksandr, Tumanov Vladimir   +2 more
doaj   +1 more source

Using Artificial Intelligence in Wireless Sensor Routing Protocols [PDF]

, 2006
This paper represents a dissertation about how an artificial intelligence technique can be applied to wireless sensor networks. Due to the constraints on data processing and power consumption, the use of artificial intelligence has been historically ...
Barbancho Concejero, Antonio, Barbancho Concejero, Julio, León de Mora, Carlos, Molina Cantero, Francisco Javier   +3 more
core   +1 more source

Dissecting the Biological Motherboard (Systems Biology and Beyond) [PDF]

, 2008
Genome-scale molecular networks, including gene pathways, gene regulatory networks and protein interactions, are central to the investigation of the nascent disciplines of systems biology and bio-complexity.
Abhay Krishna, Ajit Narayanan
core   +2 more sources