Ríchard R. Carrillo

Ríchard R. Carrillo

Assistant Professor

University of Granada

Richard R. Carrillo is a postodoctoral fellow in the Research Center for Information and Communications Technologies of the University of Granada (CITIC-UGR). His research interests include computational neuroscience and efficient network-simulation methods, with a main focus on real-time simulation of biologically-inspired neural networks and its application to robot control.

More specifically, he has conducted research on event-driven simulation schemes, cerebellar modeling and plasticity, network synchronicity, control of robotic arm trajectory and neural modeling.

Interests
  • Spiking neuronal network simulation
  • Cerebellum
  • Plasticity
  • Network synchornicity

Latest activity

Recent papers

20 of 46
  1. A cerebellar-based solution to the nondeterministic time delay problem in robotic control
  2. On the Use of a Multimodal Optimizer for Fitting Neuron Models. Application to the Cerebellar Granule Cell
  3. Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation
  4. A Metric for Evaluating Neural Input Representation in Supervised Learning Networks
  5. Event-and time-driven techniques using parallel CPU-GPU co-processing for spiking neural networks
  6. Distributed Cerebellar Motor Learning: A Spike-Timing-Dependent Plasticity Model
  7. A spiking neural simulator integrating event-driven and time-driven computation schemes using parallel CPU-GPU co-processing: a case study
  8. Integrated neural and robotic simulations. Simulation of cerebellar neurobiological substrate for an object-oriented dynamic model abstraction process
  9. Adaptive robotic control driven by a versatile spiking cerebellar network
  10. Fast convergence of learning requires plasticity between inferior olive and deep cerebellar nuclei in a manipulation task: a closed-loop robotic simulation
  11. Dynamics Model Abstraction Scheme Using Radial Basis Functions
  12. Bottom-up visual attention model based on FPGA
  13. Adaptive cerebellar spiking model embedded in the control loop: context switching and robustness against noise
  14. Cerebellar input configuration toward object model abstraction in manipulation tasks
  15. Context separability mediated by the granular layer in a spiking cerebellum model for robot control
  16. Event and time driven hybrid simulation of spiking neural networks
  17. Cerebellarlike corrective model inference engine for manipulation tasks
  18. Cerebellar spiking engine: Towards objet model abstraction in manipulation
  19. Local image phase, energy and orientation extraction using FPGAs
  20. Event-driven simulation of cerebellar granule cells
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Projects

4
  1. BRAINMOVE - Neural Sensorimotor Modelling for Adaptive Movement Control
  2. HumAMI - Accelerating European AI Innovation, Cooperation and Industrial Adoption
  3. SENSCOMP - Simulation of the neural computational substrate in the sensorimotor system. Adaptation mechanisms and their integration in experimental platforms
  4. DLROB - Deep Learning for accurate movement of collaborative robotics
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