Learning invariance from transformation sequences
Neural Computation
Navigating through temporal difference
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
A hard wired model of coupled frontal working memories for various tasks
Information Sciences: an International Journal
A Theory of the Striatum
Journal of Cognitive Neuroscience
A Computational Model of How the Basal Ganglia Produce Sequences
Journal of Cognitive Neuroscience
Model of Cue Extraction from Distractors by Active Recall
Neural Information Processing
Goal-directed feature learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Self-organization is one of fundamental brain computations for forming efficient representations of information. Experimental support for this idea has been largely limited to the developmental and reorganizational formation of neural circuits in the sensory cortices. We now propose that self-organization may also play an important role in short-term synaptic changes in reward-driven voluntary behaviors. It has recently been shown that many neurons in the basal ganglia change their sensory responses flexibly in relation to rewards. Our computational model proposes that the rapid changes in striatal projection neurons depend on the subtle balance between the Hebb-type mechanisms of excitation and inhibition, which are modulated by reinforcement signals. Simulations based on the model are shown to produce various types of neural activity similar to those found in experiments.