Abstract
We present a neuro-computational model that, based on brain principles, succeeds in performing a category learning task. In particular, the network includes a fast learner (the basal ganglia) that via reinforcement learns to execute the task, and a slow learner (the prefrontal cortex) that can acquire abstract representations from the accumulation of experiences and ultimately pushes the task level performance to higher levels.
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Acknowledgments
This work has been funded by DFG HA2630/4-1 and in part by DFG HA2630/8-1.
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Villagrasa, F., Baladron, J., Hamker, F.H. (2016). Fast and Slow Learning in a Neuro-Computational Model of Category Acquisition. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_29
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DOI: https://doi.org/10.1007/978-3-319-44778-0_29
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