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Nano-Modeling and Computation in Bio and Brain Dynamics

The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in th...

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Detalles Bibliográficos
Autores principales: Di Sia, Paolo, Licata, Ignazio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597135/
https://www.ncbi.nlm.nih.gov/pubmed/28952573
http://dx.doi.org/10.3390/bioengineering3020011
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author Di Sia, Paolo
Licata, Ignazio
author_facet Di Sia, Paolo
Licata, Ignazio
author_sort Di Sia, Paolo
collection PubMed
description The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in the inner brain parameters, in order to mimic the external transformation by the proper use of sensors and effectors. This paper highlights some crossing research areas of natural computing, nanotechnology, and brain modeling and considers two interesting theoretical approaches related to brain dynamics: (a) the memory in neural network, not as a passive element for storing information, but integrated in the neural parameters as synaptic conductances; and (b) a new transport model based on analytical expressions of the most important transport parameters, which works from sub-pico-level to macro-level, able both to understand existing data and to give new predictions. Complex biological systems are highly dependent on the context, which suggests a “more nature-oriented” computational philosophy.
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spelling pubmed-55971352017-09-21 Nano-Modeling and Computation in Bio and Brain Dynamics Di Sia, Paolo Licata, Ignazio Bioengineering (Basel) Article The study of brain dynamics currently utilizes the new features of nanobiotechnology and bioengineering. New geometric and analytical approaches appear very promising in all scientific areas, particularly in the study of brain processes. Efforts to engage in deep comprehension lead to a change in the inner brain parameters, in order to mimic the external transformation by the proper use of sensors and effectors. This paper highlights some crossing research areas of natural computing, nanotechnology, and brain modeling and considers two interesting theoretical approaches related to brain dynamics: (a) the memory in neural network, not as a passive element for storing information, but integrated in the neural parameters as synaptic conductances; and (b) a new transport model based on analytical expressions of the most important transport parameters, which works from sub-pico-level to macro-level, able both to understand existing data and to give new predictions. Complex biological systems are highly dependent on the context, which suggests a “more nature-oriented” computational philosophy. MDPI 2016-04-05 /pmc/articles/PMC5597135/ /pubmed/28952573 http://dx.doi.org/10.3390/bioengineering3020011 Text en © 2016 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Di Sia, Paolo
Licata, Ignazio
Nano-Modeling and Computation in Bio and Brain Dynamics
title Nano-Modeling and Computation in Bio and Brain Dynamics
title_full Nano-Modeling and Computation in Bio and Brain Dynamics
title_fullStr Nano-Modeling and Computation in Bio and Brain Dynamics
title_full_unstemmed Nano-Modeling and Computation in Bio and Brain Dynamics
title_short Nano-Modeling and Computation in Bio and Brain Dynamics
title_sort nano-modeling and computation in bio and brain dynamics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5597135/
https://www.ncbi.nlm.nih.gov/pubmed/28952573
http://dx.doi.org/10.3390/bioengineering3020011
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