Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a graphical manner, and automatically transforming them into an executable format and performing simulation experiments. The temporal-causal network modelling format used makes it easy to take into account theories and findings about complex brain processes known from Cognitive, Affective and Social Neuroscience, which, for example, often involve dynamics based on interrelating cycles. This enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.
Original languageEnglish
Pages (from-to)131-168
Number of pages38
JournalBiologically Inspired Cognitive Architectures
Volume16
DOIs
Publication statusPublished - 1 Apr 2016

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Mental Processes
Neurosciences
Numerical models
Brain
Emotions
Differential equations
Software
Experiments

Keywords

  • Causal
  • Dynamic
  • Modeling
  • State-determined system
  • Temporal

Cite this

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title = "Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach",
abstract = "This paper presents a dynamic modelling approach that enables to design complex high level conceptual representations of models in the form of causal-temporal networks, which can be automatically transformed into executable numerical model representations. Dedicated software is available to support designing models in a graphical manner, and automatically transforming them into an executable format and performing simulation experiments. The temporal-causal network modelling format used makes it easy to take into account theories and findings about complex brain processes known from Cognitive, Affective and Social Neuroscience, which, for example, often involve dynamics based on interrelating cycles. This enables to address complex phenomena such as the integration of emotions within all kinds of cognitive processes, and of internal simulation and mirroring of mental processes of others. In this paper also the applicability has been discussed in general terms, showing for example that every process that can be modelled by first-order differential equations, also can be modeled by the presented temporal-causal network modeling apporoach. A variety of example models that can be found in other papers illustrate the applicability of the approach in more detail.",
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Dynamic Modeling Based on a Temporal-Causal Network Modeling Approach. / Treur, J.

In: Biologically Inspired Cognitive Architectures, Vol. 16, 01.04.2016, p. 131-168.

Research output: Contribution to JournalArticleAcademicpeer-review

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