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Bioengineering 2016, 3(2), 11; doi:10.3390/bioengineering3020011

Nano-Modeling and Computation in Bio and Brain Dynamics

1,2,†,* and 2,3,†
1
Department of Philosophy, Education and Psychology, University of Verona, Lungadige Porta Vittoria 17, Verona 37129, Italy
2
ISEM, Institute for Scientific Methodology, Palermo 90146, Italy
3
School of Advanced International Studies on Applied Theoretical and Non Linear Methodologies in Physics, Bari 70121, Italy
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Gou-Jen Wang
Received: 14 November 2015 / Revised: 17 March 2016 / Accepted: 29 March 2016 / Published: 5 April 2016
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Abstract

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. View Full-Text
Keywords: neuro-nanoscience; cognitive science; bioengineering; brain; carrier transport; theoretical modeling; neural geometry; memristor; electrical circuits neuro-nanoscience; cognitive science; bioengineering; brain; carrier transport; theoretical modeling; neural geometry; memristor; electrical circuits
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Di Sia, P.; Licata, I. Nano-Modeling and Computation in Bio and Brain Dynamics. Bioengineering 2016, 3, 11.

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