Fast and Slow Learning in a Neuro-Computational Model of Category Acquisition

  • Francesc Villagrasa
  • Javier Baladron
  • Fred H. Hamker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9886)


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.


Categorization Basal ganglia Fast-learner Reinforcement learning Prefrontal cortex Slow-learner 



This work has been funded by DFG HA2630/4-1 and in part by DFG HA2630/8-1.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Francesc Villagrasa
    • 1
  • Javier Baladron
    • 1
  • Fred H. Hamker
    • 1
  1. 1.Artificial IntelligenceChemnitz University of TechnologyChemnitzGermany

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