scholarly journals Boolean Biology: Introducing Boolean Networks and Finite Dynamical Systems Models to Biology and Mathematics Courses

2011 ◽  
Vol 6 (6) ◽  
pp. 39-60 ◽  
Author(s):  
R. Robeva ◽  
B. Kirkwood ◽  
R. Davies
NeuroImage ◽  
2011 ◽  
Vol 54 (2) ◽  
pp. 807-823 ◽  
Author(s):  
Srikanth Ryali ◽  
Kaustubh Supekar ◽  
Tianwen Chen ◽  
Vinod Menon

2017 ◽  
Author(s):  
Wayne M. Getz ◽  
Richard Salter ◽  
Oliver Muellerklein ◽  
Hyun S. Yoon ◽  
Krti Tallam

AbstractEpidemiological models are dominated by SEIR (Susceptible, Exposed, Infected and Removed) dynamical systems formulations and their elaborations. These formulations can be continuous or discrete, deterministic or stochastic, or spatially homogeneous or heterogeneous, the latter often embracing a network formulation. Here we review the continuous and discrete deterministic and discrete stochastic formulations of the SEIR dynamical systems models, and we outline how they can be easily and rapidly constructed using the Numerus Model Builder, a graphically-driven coding platform. We also demonstrate how to extend these models to a metapopulation setting using both the Numerus Model Builder network and geographical mapping tools.


2001 ◽  
Vol 24 (1) ◽  
pp. 50-51 ◽  
Author(s):  
Arthur B. Markman

The proposed model is put forward as a template for the dynamical systems approach to embodied cognition. In order to extend this view to cognitive processing in general, however, two limitations must be overcome. First, it must be demonstrated that sensorimotor coordination of the type evident in the A-not-B error is typical of other aspects of cognition. Second, the explanatory utility of dynamical systems models must be clarified.


10.37236/7017 ◽  
2018 ◽  
Vol 25 (3) ◽  
Author(s):  
Maximilien Gadouleau

A finite dynamical system is a function $f : A^n \to A^n$ where $A$ is a finite alphabet, used to model a network of interacting entities. The main feature of a finite dynamical system is its interaction graph, which indicates which local functions depend on which variables; the interaction graph is a qualitative representation of the interactions amongst entities on the network. The rank of a finite dynamical system is the cardinality of its image; the periodic rank is the number of its periodic points. In this paper, we determine the maximum rank and the maximum periodic rank of a finite dynamical system with a given interaction graph over any non-Boolean alphabet. The rank and the maximum rank are both computable in polynomial time. We also obtain a similar result for Boolean finite dynamical systems (also known as Boolean networks) whose interaction graphs are contained in a given digraph. We then prove that the average rank is relatively close (as the size of the alphabet is large) to the maximum. The results mentioned above only deal with the parallel update schedule. We finally determine the maximum rank over all block-sequential update schedules and the supremum periodic rank over all complete update schedules.


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