A model of reaching that integrates reinforcement learning and population encoding of postures (Articolo in rivista)

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Label
  • A model of reaching that integrates reinforcement learning and population encoding of postures (Articolo in rivista) (literal)
Anno
  • 2006-01-01T00:00:00+01:00 (literal)
Alternative label
  • Ognibene D., Rega A., Baldassarre G. (2006)
    A model of reaching that integrates reinforcement learning and population encoding of postures
    in Lecture notes in computer science; Springer-Verlag, Berlin (Germania)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Ognibene D., Rega A., Baldassarre G. (literal)
Pagina inizio
  • 381 (literal)
Pagina fine
  • 393 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 4095 (literal)
Rivista
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  • 13 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di scienze e tecnologie della cognizione (literal)
Titolo
  • A model of reaching that integrates reinforcement learning and population encoding of postures (literal)
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 re-ward-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 capa-ble 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 rein-forcement 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 primi-tives from the postures' continuous space on the basis of their population encoding. (literal)
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