Open AccessPhase diagrams and dynamics of a computationally efficient map-based neuron model
Author(s) -
Mauricio GirardiSchappo,
G. S. Bortolotto,
R. V. Stenzinger,
Jheniffer J. Gonsalves,
M. H. R. Tragtenberg
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0174621
Subject(s) - aperiodic graph , parameter space , dynamical systems theory , bifurcation , statistical physics , biological neuron model , phase space , computer science , physics , phase diagram , perceptron , algorithm , biological system , phase (matter) , artificial intelligence , artificial neural network , mathematics , biology , geometry , combinatorics , nonlinear system , quantum mechanics , thermodynamics
We introduce a new map-based neuron model derived from the dynamical perceptron family that has the best compromise between computational efficiency, analytical tractability, reduced parameter space and many dynamical behaviors. We calculate bifurcation and phase diagrams analytically and computationally that underpins a rich repertoire of autonomous and excitable dynamical behaviors. We report the existence of a new regime of cardiac spikes corresponding to nonchaotic aperiodic behavior. We compare the features of our model to standard neuron models currently available in the literature.