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Authors: Oussama H. Hamid 1 and Jochen Braun 2

Affiliations: 1 University of Kurdistan Hewlêr, Iraq ; 2 Otto-von-Guericke University, Germany

ISBN: 978-989-758-274-5

Keyword(s): Attractor Neural Networks, Model-Based and Model-Free Reinforcement Learning, Stability-Plasticity Dilemma, Multiple Brain Systems, Temporal Statistics.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Fuzzy Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Reinforcement Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Soft Computing and Intelligent Agents ; Theory and Methods

Abstract: It is widely accepted that reinforcement learning (RL) mechanisms are optimal only if there is a predefined set of distinct states that are predictive of reward. This poses a cognitive challenge as to which events or combinations of events could potentially predict reward in a non-stationary environment. In addition, the computational discrepancy between two families of RL algorithms, model-free and model-based RL, creates a stability-plasticity dilemma, which in the case of interactive and competitive multiple brain systems poses a question of how to guide optimal decision-making control when there is competition between two systems implementing different types of RL methods. We argue that both computational and cognitive challenges can be met by infusing the RL framework as an algorithmic theory of human behavior with the strengths of the attractor framework at the level of neural implementation. Our position is supported by the hypothesis that ‘attractor states’ which are stable patterns of self-sustained and reverberating brain activity, are a manifestation of the collective dynamics of neuronal populations in the brain. Hence, when neuronal activity is described at an appropriate level of abstraction, simulations of spiking neuronal populations capture the collective dynamics of the network in response to recurrent interactions between these populations. (More)

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Paper citation in several formats:
Hamid, O. H. and Braun, J. (2017). Attractor Neural States: A Brain-Inspired Complementary Approach to Reinforcement Learning.In Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-274-5, pages 385-392. DOI: 10.5220/0006580203850392

@conference{ijcci17,
author={Hamid, O. H. and Jochen Braun.},
title={Attractor Neural States: A Brain-Inspired Complementary Approach to Reinforcement Learning},
booktitle={Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2017},
pages={385-392},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006580203850392},
isbn={978-989-758-274-5},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Attractor Neural States: A Brain-Inspired Complementary Approach to Reinforcement Learning
SN - 978-989-758-274-5
AU - Hamid, O. H.
AU - Braun, J.
PY - 2017
SP - 385
EP - 392
DO - 10.5220/0006580203850392

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