A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Plasticity is a crucial adaptive characteristic of the brain. Relatively recently mecha-nisms have been found showing that plasticity itself is controlled by what is called metaplasticity. In this paper a modeling environment is introduced to develop and simulate reified temporal-causal network models that can be applied for cognitive agent models. It is shown how this environment is a useful tool to model plasticity combined with metaplasticity. The model shows how in a context-sensitive, dy-namic manner learning can be accelerated, but also can be reduced to obtain a stable situation.
Original languageEnglish
Title of host publicationProc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19
PublisherSpringer
Publication statusPublished - 28 Oct 2019

Publication series

NameLecture Notes in Artificial Intelligence

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Plasticity
Brain

Cite this

Treur, J. (2019). A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models. In Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19 (Lecture Notes in Artificial Intelligence). Springer.
Treur, Jan. / A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models. Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer, 2019. (Lecture Notes in Artificial Intelligence).
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abstract = "Plasticity is a crucial adaptive characteristic of the brain. Relatively recently mecha-nisms have been found showing that plasticity itself is controlled by what is called metaplasticity. In this paper a modeling environment is introduced to develop and simulate reified temporal-causal network models that can be applied for cognitive agent models. It is shown how this environment is a useful tool to model plasticity combined with metaplasticity. The model shows how in a context-sensitive, dy-namic manner learning can be accelerated, but also can be reduced to obtain a stable situation.",
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Treur, J 2019, A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models. in Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Lecture Notes in Artificial Intelligence, Springer.

A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models. / Treur, Jan.

Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer, 2019. (Lecture Notes in Artificial Intelligence).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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Treur J. A Modeling Environment for Reified Temporal-Causal Networks Modeling Plasticity and Metaplasticity in Cognitive Agent Models. In Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer. 2019. (Lecture Notes in Artificial Intelligence).