Abstract
In this paper two neural models of basal ganglia function during motor sequential behaviour are presented. Two connectionist models of neuron — like elements that mimic some aspects of anatomy and physiology of cortico — basal ganglia — thalamo - cortical loops have been developed. The aim of this work is to report a new computational model of motor sequence learning guided by reinforcement signals from neuronal systems that evaluate behaviours. The models are partially recurrent neural networks known as Jordan networks trained under a reinforcement learning paradigm. To validate these models, experimental findings of Tanji and Shima [5] on monkeys have been reviewed. The hypothesis that cortico #x2014; basal ganglionic loops learn and perform sequences successfully driven by Reinforcement signals has been demonstrated in computer simulations of the models Presented in this paper.
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References
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© 2001 Springer-Verlag Berlin Heidelberg
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Vilaplana, J.M., Batlle, J.F., Coronado, J.L. (2001). Connectionist Models of Cortico-Basal Ganglia Adaptive Neural Networks During Learning of Motor Sequential Procedures. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_46
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DOI: https://doi.org/10.1007/3-540-45720-8_46
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