Dynamical Complexity in Cognitive Neural Networks

In the last twenty years an important effort in brain sciences, especially in cognitive science, has been the development of mathematical tool that can deal with the complexity of extensive recordings corresponding to the neuronal activity obtained from hundreds of neurons. We discuss here along wit...

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Autores principales:	GOLES,ERIC, PALACIOS,ADRIÁN G
Lenguaje:	English
Publicado:	Sociedad de Biología de Chile 2007
Materias:	Artificial Neural Net Brain Dynamical Complexity Computational Neurosciences Cellular Automata
Acceso en línea:	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500009
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spelling	oai:scielo:S0716-976020070005000092008-05-28Dynamical Complexity in Cognitive Neural NetworksGOLES,ERICPALACIOS,ADRIÁN G Artificial Neural Net Brain Dynamical Complexity Computational Neurosciences Cellular Automata In the last twenty years an important effort in brain sciences, especially in cognitive science, has been the development of mathematical tool that can deal with the complexity of extensive recordings corresponding to the neuronal activity obtained from hundreds of neurons. We discuss here along with some historical issues, advantages and limitations of Artificial Neural Networks (ANN) that can help to understand how simple brain circuits work and whether ANN can be helpful to understand brain neural complexityinfo:eu-repo/semantics/openAccessSociedad de Biología de ChileBiological Research v.40 n.4 20072007-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500009en10.4067/S0716-97602007000500009
institution	Scielo Chile
collection	Scielo Chile
language	English
topic	Artificial Neural Net Brain Dynamical Complexity Computational Neurosciences Cellular Automata
spellingShingle	Artificial Neural Net Brain Dynamical Complexity Computational Neurosciences Cellular Automata GOLES,ERIC PALACIOS,ADRIÁN G Dynamical Complexity in Cognitive Neural Networks
description	In the last twenty years an important effort in brain sciences, especially in cognitive science, has been the development of mathematical tool that can deal with the complexity of extensive recordings corresponding to the neuronal activity obtained from hundreds of neurons. We discuss here along with some historical issues, advantages and limitations of Artificial Neural Networks (ANN) that can help to understand how simple brain circuits work and whether ANN can be helpful to understand brain neural complexity
author	GOLES,ERIC PALACIOS,ADRIÁN G
author_facet	GOLES,ERIC PALACIOS,ADRIÁN G
author_sort	GOLES,ERIC
title	Dynamical Complexity in Cognitive Neural Networks
title_short	Dynamical Complexity in Cognitive Neural Networks
title_full	Dynamical Complexity in Cognitive Neural Networks
title_fullStr	Dynamical Complexity in Cognitive Neural Networks
title_full_unstemmed	Dynamical Complexity in Cognitive Neural Networks
title_sort	dynamical complexity in cognitive neural networks
publisher	Sociedad de Biología de Chile
publishDate	2007
url	http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0716-97602007000500009
work_keys_str_mv	AT goleseric dynamicalcomplexityincognitiveneuralnetworks AT palaciosadriang dynamicalcomplexityincognitiveneuralnetworks
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Dynamical Complexity in Cognitive Neural Networks

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