Human sleep and circadian rhythms: a simple model based on two coupled oscillators

1987 ◽  
Vol 25 (3) ◽  
pp. 327-347 ◽  
Author(s):  
Steven H. Strogatz
Keyword(s):  
Simple Model ◽  
Circadian Rhythms ◽  
Coupled Oscillators ◽  
Model Based ◽  
Human Sleep

A numerical and experimental study of a simple model-based predictive control strategy in a perimeter zone with phase change material

2018 ◽  
Vol 24 (9) ◽  
pp. 933-944 ◽  
Author(s):  
Anastasios C. Papachristou ◽  
Charalampos A. Vallianos ◽  
Vasken Dermardiros ◽  
Andreas K. Athienitis ◽  
JosÉ A. Candanedo
Keyword(s):  
Experimental Study ◽  
Simple Model ◽  
Phase Change ◽  
Phase Change Material ◽  
Predictive Control ◽  
Control Strategy ◽  
Model Based ◽  
Change Material

scholarly journals Ecological mechanisms in cognitive science

2019 ◽  
Vol 29 (5) ◽  
pp. 676-696 ◽  
Author(s):  
Sabrina Golonka ◽  
Andrew D. Wilson
Keyword(s):  
Simple Model ◽  
Circadian Rhythms ◽  
Cognitive Science ◽  
Real Time ◽  
Mechanistic Model ◽  
Mechanistic Explanation ◽  
Perceptual Information ◽  
Proof Of Concept ◽  
Time Interaction ◽  
Ecological Mechanisms

In 2010, Bechtel and Abrahamsen defined and described what it means to be a dynamic causal mechanistic explanatory model. They discussed the development of a mechanistic explanation of circadian rhythms as an exemplar of the process and challenged cognitive science to follow this example. This article takes on that challenge. A mechanistic model is one that accurately represents the real parts and operations of the mechanism being studied. These real components must be identified by an empirical programme that decomposes the system at the correct scale and localises the components in space and time. Psychological behaviour emerges from the nature of our real-time interaction with our environments—here we show that the correct scale to guide decomposition is picked out by the ecological perceptual information that enables that interaction. As proof of concept, we show that a simple model of coordinated rhythmic movement, grounded in information, is a genuine dynamical mechanistic explanation of many key coordination phenomena.

scholarly journals Learning to Coordinate Efficiently: A Model-based Approach

2003 ◽  
Vol 19 ◽  
pp. 11-23 ◽  
Author(s):  
R. I. Brafman ◽  
M. Tennenholtz
Keyword(s):  
Reinforcement Learning ◽  
Simple Model ◽  
Stochastic Games ◽  
Convergence Rates ◽  
Learning Algorithms ◽  
Common Interest ◽  
Model Based ◽  
Optimal Value ◽  
To Receive

In common-interest stochastic games all players receive an identical payoff. Players participating in such games must learn to coordinate with each other in order to receive the highest-possible value. A number of reinforcement learning algorithms have been proposed for this problem, and some have been shown to converge to good solutions in the limit. In this paper we show that using very simple model-based algorithms, much better (i.e., polynomial) convergence rates can be attained. Moreover, our model-based algorithms are guaranteed to converge to the optimal value, unlike many of the existing algorithms.

scholarly journals The Effect of a Close Binary Companion on a Nova Outburst

1979 ◽  
Vol 53 ◽  
pp. 531-531
Author(s):  
Jim MacDonald
Keyword(s):  
Simple Model ◽  
White Dwarf ◽  
Energy Generation ◽  
Close Binary ◽  
Additional Energy ◽  
Model Based ◽  
Hydrodynamic Calculations ◽  
Cno Abundances ◽  
Nova Outburst

We compare two hydrodynamic calculations of thermonuclear runaways in material accreted by a 1M⊙ white dwarf of initial luminosity 10−3L⊙. In both cases the CNO abundances are taken to be near solar (ZCNO = 0.014). The only difference between the calculations is that in one sequence of models (seq.B) the additional energy generation due to the interaction between the expanding nova envelope and a close red dwarf companion is allowed for, using a simple model based on that of Paczynski (1976).

The dynamics of language

1999 ◽  
Vol 22 (2) ◽  
pp. 284-285
Author(s):  
Peter W. Culicover ◽  
Andrzej Nowak
Keyword(s):  
Phase Space ◽  
Dynamical Systems ◽  
Simple Model ◽  
Syntactic Structure ◽  
Neural Assemblies ◽  
Systems Perspective ◽  
Model Based ◽  
Associative Structures

To deal with syntactic structure, one needs to go beyond a simple model based on associative structures, and to adopt a dynamical systems perspective, where each phrase and sentence of a language is represented as a trajectory in a syntactic phase space. Neural assemblies could possibly be used to produce dynamics that in principle could handle syntax along these lines.

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