Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Actor-critic models of the basal ganglia: new anatomical and computational perspectives
Neural Networks - Computational models of neuromodulation
Neural Networks - 2006 Special issue: Neurobiology of decision making
A computational model for the effect of dopamine on action selection during stroop test
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
A neurocomputational model of nicotine addiction based on reinforcement learning
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
From occasional choices to inevitable musts: a computational model of nicotine addiction
Computational Intelligence and Neuroscience
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A connectionist model of cortico-striato-thalamic loops unifying learning and action selection is proposed. The aim in proposing the connectionist model is to develop a simple model revealing the mechanisms behind the cognitive process of goal directed behaviour rather than merely obtaining a model of neural structures. In the proposed connectionist model, the action selection is realized by a non-linear dynamical system, while learning that modifies the action selection is realized similar to actor-critic model of reinforcement learning. The task of sequence learning is solved with the proposed model to make clear how the model can be implemented.