1 / 42

Coupled Spiking Oscillators Constructed with Integrate-and-Fire Neural Networks

Coupled Spiking Oscillators Constructed with Integrate-and-Fire Neural Networks. Ralph Etienne-Cummings, Francesco Tenore, Jacob Vogelstein Johns Hopkins University, Baltimore, MD Collaborators: M. Anthony Lewis, Iguana Robotics Inc, Urbana, IL

ide
Download Presentation

Coupled Spiking Oscillators Constructed with Integrate-and-Fire Neural Networks

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Coupled Spiking Oscillators Constructed with Integrate-and-Fire Neural Networks Ralph Etienne-Cummings, Francesco Tenore, Jacob Vogelstein Johns Hopkins University, Baltimore, MD Collaborators: M. Anthony Lewis, Iguana Robotics Inc, Urbana, IL Avis Cohen, University of Maryland, College Park, MD Sponsored by ONR, NSF, SRC

  2. Descending signals Why do we need coupled Oscillators? • The Central Pattern Generator is the heart of locomotion controllers • What is a Central Pattern Generator for Locomotion? • Collection of recurrently coupled neurons which can function autonomously • All fast moving animals (Swimming, running, flying) use a CPG for locomotion

  3. (Star Wars, Lucas Films) Applications: Biomorphic Robots (IS Robotics, Inc.)

  4. Applications: Physical Augmentation • Neural prosthesis for spinal cord patients • Artificial limbs for amputees • Exoskeletons for enhanced load carrying, running and jumping

  5. Applications: Physical Augmentation • Neural prosthesis for spinal cord patients Cleveland FES Center, Case-Western Reserve U.

  6. CPG Control Locomotion Across Species Lamprey Swimming Mellen et al., 1995 Complete SCI Human Dimitrijevic et al., 1998 Spinal Cat Walking on Treadmill Grillner and Zangger, 1984

  7. Lamprey with Spinal Transections After Complete Transection of SC Cohen et al., 1987 Dysfunctional Swimming after Regeneration Cohen et al., 1999

  8. Determining the Structure and PTC/PRC of the CPG Simple Lamprey CPG Model Lasner et al., 1998 Schematic of Spinal Coordination Experiment Complex Lamprey CPG Model Boothe and Cohen, 2003 Neural Stimulators, Recording & Control Set-up

  9. Implementation of CPG Locomotory Controller

  10. Locomotory Requirements • A self-sustained unit for providing the control timings to limbs. (CPG) • Adaptive capability to correct for asymmetries and noise in limbs. (Local adaptation) • Reactive capability to handle non-ideal environmental conditions. (Reflex & recovery from perturbation) • Local sensory network to asses the dynamic state of the limbs. (Joint and muscle receptors) • Descending control signals to include intent, long-term learning and smooth transitions in the behaviors. (Motor, cerebellum & sensory cortex)

  11. Adaptive and Autonomous Control of Running Legs Set the center of the limb swing Set the angular width of a stride Set the frequency of strides

  12. Sensory Adaptation Implementation

  13. Hardware Implementation: Integrate-and-Fire Array Basic neuron element: Integrate-and-fire Synapse Array Neurons 10 Neurons, 18 synapse/neuron Neuron architecture

  14. CPG based Running Reality Check

  15. CPG Controller with Sensory Feedback Passive Knee joint Driven Treadmill Mechanical Harness

  16. CPG based Running

  17. Experiments

  18. Experiment 1: Lesion Experiments Sensory Feedback is Lesioned Light ON: Sensory Feedback intact Light OFF: Sensory Feedback Cut

  19. Does 1.5 Mono-peds ~ One Bi-ped?

  20. Serendipitous Gaits ‘Ballet Dancer’ ‘Strauss’

  21. ‘Other Gait…’ ‘Night on the town’

  22. Two Mono-peds -- One Bi-ped

  23. Two Mono-peds to make One Bi-ped Uncoupled: Right - Bad gait Left - Good gait Coupled: Inhibition Asymmetric Weights

  24. Sensory Feedback Mediated Motor Neuron Spike Rate Adaptation (A1 Reflex)

  25. How do we couple these oscillators: Spike Based Coupling

  26. Membrane Equation and Spike Coupling Membrane equations Direct Coupling Weight of Impulse L Phase update due to coupling Spike Coupling

  27. Geometry of Coupling …..Single Pulse coupling Via Analysis Collected Data on CPG Chip

  28. Geometry of Coupling ….. 2 Spike Coupling Theoretical Prediction Measured Data

  29. Multiple Spike Coupling

  30. Measured PTC and PRC for Lamprey SC J. Vogelstein et al, 2004 (unpublished)

  31. Measured PTC and PRC for Lamprey SC J. Vogelstein et al, 2004 (unpublished)

  32. Hardware Implementation: Integrate-and-Fire Array Basic neuron element: Integrate-and-fire Synapse Array Neurons 10 Neurons, 18 synapse/neuron Neuron architecture

  33. Coupling with Linear and Non-Linear Synapses • Uncoupled neurons • Excitatory linear or nonlinear synaptic current inputs • Discharging currents

  34. Coupling with Linear and Non-Linear Synapses Membrane potential

  35. Firing Rates Firing rates versus current inputs for linear and nonlinear synapses

  36. Coupled Neurons • Mutually coupled neurons using linear and nonlinear synapses • Firing rates versus strength of the coupling • Nonlinear synapse provides a larger phase locking region

  37. Entrainment using Spike Coupling and Non-Linear Synapses Purpose: • to make two oscillators of different frequencies sync up • to be able to control the phase delay between them at will

  38. Entrainment • Phase delay function of weight: • Strong weight --> small delay • Weak weight --> large delay • ~ 0 - 180° attainable • Finer tuning possible for lower phase delays

  39. Emulation of waveforms required for biped locomotion Using described technique, waveforms for different robotic limbs can be created

  40. Emulation of waveforms required for biped locomotion Using described technique, waveforms for different robotic limbs can be created

  41. Summary • An integrate-and-fire neuron array is used to realize a CPG controller for a biped • Sensory feedback to CPG controllers allows a biped to adapt for mismatches in actuators and environmental perturbation • Individual CPG oscillators per limb are coupled to create a biped controller • Spike based coupling offer a more controlled and faster way to synchronize oscillators • Non-linear synaptic currents (as a function of membrane potential) allow robust phase locking between oscillators • Arbitrary phase locking between oscillators can be realized for CPG controllers • Spike coupled oscillators can be used to generate control signals for more bio-realistic biped and quadrupeds • We are conducting the early experiments to control spinal CPG circuits which will allow us to bridge the gap between two pieces of transected spinal cord. Iguana Robotics’ Snappy Iguana Robotics’ TomCat

  42. Summary Lewis, Etienne-Cummings, Hartmann, Cohen, and Xu, “An In Silico Central Pattern Generator: Silicon Oscillator, Coupling, Entrainment, Physical Computation & Biped Mechanism Control,” Biological Cybernetics, Vol. 88, No. 2, pp 137-151, Feb. 2003. URLs: http://etienne.ece.jhu.edu/ http://www.iguana-robotics.com http://www.life.umd.edu/biology/cohenlab/ http://www.ine-web.org

More Related