A Model of Reaching that Integrates Reinforcement Learning and Population Encoding of Postures

  • Dimitri Ognibene
  • Angelo Rega
  • Gianluca Baldassarre
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)

Abstract

When monkeys tackle novel complex behavioral tasks by trial-and-error they select actions from repertoires of sensorimotor primitives that allow them to search solutions in a space which is coarser than the space of fine movements. Neuroscientific findings suggested that upper-limb sensorimotor primitives might be encoded, in terms of the final goal-postures they pursue, in premotor cortex. A previous work by the authors reproduced these results in a model based on the idea that cortical pathways learn sensorimotor primitives while basal ganglia learn to assemble and trigger them to pursue complex reward-based goals. This paper extends that model in several directions: a) it uses a Kohonen network to create a neural map with population encoding of postural primitives; b) it proposes an actor-critic reinforcement learning algorithm capable of learning to select those primitives in a biologically plausible fashion (i.e., through a dynamic competition between postures); c) it proposes a procedure to pre-train the actor to select promising primitives when tackling novel reinforcement learning tasks. Some tests (obtained with a task used for studying monkeys engaged in learning reaching-action sequences) show that the model is computationally sound and capable of learning to select sensorimotor primitives from the postures’ continuous space on the basis of their population encoding.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Dimitri Ognibene
    • 1
  • Angelo Rega
    • 1
  • Gianluca Baldassarre
    • 1
  1. 1.Laboratory of Autonomous Robotics and Artificial Life, Istituto di Scienze e Tecnologie della CognizioneConsiglio Nazionale delle Ricerche (LARAL-ISTC-CNR)RomaItaly

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