ADAPTIVE CEREBELLAR SPIKING MODEL EMBEDDED IN THE CONTROL LOOP: CONTEXT SWITCHING AND ROBUSTNESS AGAINST NOISE

N. R. Luque, J. A. Garrido, R. R. Carrillo, Silvia Tolu, E. Ros

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

This work evaluates the capability of a spiking cerebellar model embedded in different loop architectures (recurrent, forward, and forward&recurrent) to control a robotic arm (three degrees of freedom) using a biologically-inspired approach. The implemented spiking network relies on synaptic plasticity (long-term potentiation and long-term depression) to adapt and cope with perturbations in the manipulation scenario: changes in dynamics and kinematics of the simulated robot. Furthermore, the effect of several degrees of noise in the cerebellar input pathway (mossy fibers) was assessed depending on the employed control architecture. The implemented cerebellar model managed to adapt in the three control architectures to different dynamics and kinematics providing corrective actions for more accurate movements. According to the obtained results, coupling both control architectures (forward&recurrent) provides benefits of the two of them and leads to a higher robustness against noise.
Original languageEnglish
JournalInternational Journal of Neural Systems
Volume21
Issue number5
Pages (from-to)385-401
ISSN0129-0657
DOIs
Publication statusPublished - 2011
Externally publishedYes

Keywords

  • Action Potentials
  • Biomechanical Phenomena
  • Cerebellum
  • Computer Simulation
  • Humans
  • Models, Neurological
  • Nerve Net
  • Neural Networks (Computer)
  • Neuronal Plasticity
  • Neurons
  • Robotics
  • Synaptic Transmission
  • COMPUTER
  • EVENT-DRIVEN SIMULATION
  • NEURAL-NETWORKS
  • PURKINJE-CELL
  • GRANULE CELLS
  • MOTOR CORTEX
  • SYNAPTIC-TRANSMISSION
  • REACHING MOVEMENTS
  • RAT CEREBELLUM
  • SYNAPSES
  • NEURONS
  • STDP
  • robot simulation
  • learning
  • biological control system
  • noise
  • Computer Networks and Communications
  • action potential
  • article
  • artificial neural network
  • biological model
  • biomechanics
  • cerebellum
  • computer simulation
  • histology
  • human
  • nerve cell
  • nerve cell network
  • nerve cell plasticity
  • physiology
  • robotics
  • synaptic transmission
  • Biomechanics
  • Biological control systems
  • Computer simulation
  • Dynamics
  • Kinematics
  • Network architecture
  • Robots
  • Robustness (control systems)
  • ROBOTS
  • Control systems

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