EventsMOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Published
with-doi10.3390/mol2net-07-12125 (registering DOI)
This submission belongs to the session 07. NICE.XSM-07: North-Ibero-America Congress on Exp. & Simul. Methods, Valencia, Spain-Miami, USA, 2021 of the event MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed.
Published date
30 Dec, 2021
Academic Editor
author-avatarHumberto Díaz
Citation
David Quesada, NEURODAT'21 IBRO-PERC Lecture on Computational Neuroscience, in Proceedings of MOL2NET'21, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 7th ed., 25 January–30 December 2021, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-07-12125
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NEURODAT'21 IBRO-PERC Lecture on Computational Neuroscience

1. Dept. of Mathematics, Miami Dade Colege (MDC), Miami, FL, USA.
Abstract

The present communication is aimed at creating the biophysical and mathematical foundations for the understanding of the current trends theory of control and networks applied to Computational Neurosciences. There are many different models of interest on this area Hodgkin – Huxley model, Fitzhugh – Nagumo model, Morris – Lecar model, Hindmarsh – Rose model, Izhikievich model, Li – Rinzel model, Wilson – Cowan model, Kuramoto model, Hopfield and Spin Glass-like models, Cellular Automata models, etc. On this presentation the focus is on this class of models and their implications/relations to computational neurosciences.

Keywords
Computational Neuroscience
Theory of Control
Complex Networks
Manuscript
Poster
DQuesada Theory of Control.pdf

NEURODAT'21 IBRO-PERC Lecture on Brain Networks Dynamics

A computational study on the catalytic mechanism of Pdx2: a glutaminase containing the Cys-His-Glu triad