Brain-based device having a cerebellar model for predictive...

Data processing: artificial intelligence – Adaptive system

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C706S045000

Reexamination Certificate

active

07827124

ABSTRACT:
A mobile brain-based device (BBD) includes a mobile platform with sensors and effectors, which is guided by a simulated nervous system that is an analogue of the cerebellar areas of the brain used for predictive motor control to determine interaction with a real-world environment. The simulated nervous system has neural areas including precerebellum nuclei (PN), Purkinje cells (PC), deep cerebellar nuclei (DCN) and an inferior olive (IO) for predicting turn and velocity control of the BBD during movement in a real-world environment. The BBD undergoes training and testing, and the simulated nervous system learns and performs control functions, based on a delayed eligibility trace learning rule.

REFERENCES:
patent: 5136687 (1992-08-01), Edelman
patent: 6169981 (2001-01-01), Werbos
patent: 6581048 (2003-06-01), Werbos
patent: 6665562 (2003-12-01), Gluckman et al.
patent: 2005/0261803 (2005-11-01), Seth
Schweighofer, et al., A Model of the Cerebellum in Adaptive Control of Saccadic Gain, Biological Cybernetics, 75, 1996, pp. 19-28.
Adelson, E. H. et al., Spatiotemporal energy models for the perception of motion, Journal of the Optical Society of America, Feb. 1985, pp. 284-299, vol. 2, No. 2.
Carey, M. R., et al., Instructive signals for motor learning from visual cortical area MT, Nature Neuroscience, Jun. 2005, pp. 813-819, vol. 8, No. 6.
Chadderton, P., et al., Integration of quanta in cerebellar granule cells during sensory processing, Nature, Apr. 22, 2004, pp. 856-860, vol. 428.
Chichilnisky, E. J. et al., Temporal resolution of ensemble visual motion signals in primate retina, Journal of Neuroscience, Jul. 30, 2003, pp. 6681-6689, vol. 23, No. 17.
Edelman, G. M., et al., Synthetic neural modeling applied to a real-world artifact, Proceedings of the National Academy of Sciences USA, Aug. 1992, pp. 7267-7271. vol. 89.
Egelhaaf, M., et al., Computational structure of a biological motion-detection system as revealed by local detector analysis in the fly's nervous system, Journal of the Optical Society of America, Jul. 1989, pp. 1070-1087, vol. 6, No. 7.
Fleischer, J. G., et al., A neurally controlled robot competes and cooperates with humans in Segway soccer, Proceedings of the 2006 IEEE International Conference on Robotics and Automation, May 2006, pp. 3673-3678, Orlando, FL.
Geisler, W. S., Motion streaks provide a spatial code for motion direction, Nature, Jul. 1, 1999, pp. 65-69, vol. 400.
Grossberg, S., The link between brain learning, attention, and consciousness, Consciousness and Cognition, 1999, pp. 1-44, vol. 8.
Grossberg, S., et al., A neural model of motion processing and visual navigation by cortical area MST, Cerebral Cortex, Dec. 1999, pp. 878-895, vol. 9.
Hansel, C., et al., Beyond parallel fiber LTD: the diversity of synaptic and non-synaptic plasticity in the cerebellum, Nature Neuroscience, May 2001, pp. 467-475, vol. 4, No. 5.
Ijspeert, A. J., et al., Simulation and robotics studies of salamander locomotion—applying neurobioloical principles to the control of locomotion in robots, Neuroinformatics, 2005, pp. 171-195, vol. 3.
Ito, M. et al., Climbing fibre induced depression of both mossy fibre responsiveness and glutamate sensitivity of cerebellar purkinje cells, Journal of Physiology, 1982, pp. 113-134, vol. 324.
Kawato, M., et al., A computational model of four regions of the cerebellum based on feedback-error learning, Biological Cybernetics, 1992, pp. 95-103, vol. 68.
Kitazawa, S., et al., Cerebellar complex spikes encode both destinations and errors in arm movements, Nature, Apr. 2, 2998, pp. 494-497, vol. 392.
Krekelberg, B., et al., Neural correlates of implied motion, Nature, Aug. 2003, pp. 674-677, vol. 424.
Krichmar, J. L., et al., Machine psychology: autonomous behavior, perceptual categorization and conditioning in a brain-based device, Cerebral Cortex, Aug. 2002, pp. 818-830, vol. 12.
Krichmar, J. L., et al., Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task, Proceedings of the National Academy of Sciences USA, Feb. 8, 2005, pp. 2111-2116, vol. 102, No. 6.
Lappe, M., A model of the combination of optic flow and extraretinal eye movement signals in primate extrastriate visual cortex—Neural model of self-motion from optic flow and extraretinal cues, Neural Networks, 1998, pp. 397-414,vol. 11.
Mauk, M. D., et al., Cerebellar function: coordination, learning or timing? Current Biology, 2000, pp. R522-R525, vol. 10.
Medina, J. F. et al., Computer simulation of cerebellar information processing, Nature Neuroscience Supplement, Nov. 2000, pp. 1205-1211, vol. 3.
Medina, J. F., et al., The representation of time for motor learning, Neuron, Jan. 6, 2005, pp. 157-167, vol. 45.
Ohyama, T., et al., Trying to understand the cerebellum well enough to build one, Ann. New York Academy of Sciences, 2002, pp. 425-438, vol. 978.
Pfeifer, R. et al., Sensory-motor coordination: the metaphor and beyond, Robotics and Autonomous Systems, 2007, pp. 157-178, vol. 20.
Prescott, T. J., et al., A robot model of the basal ganglia: behavior and intrinsic processing, Neural Networks, 2006, pp. 31-61, vol. 19.
Scudder, C. A., Role of the fastigal nucleus in controlling horizontal saccades during adaptation, Ann. New York Academy of Sciences, 2002, pp. 63-78, vol. 978.
Seth, A. K., et al., Visual binding through reentrant connectivity and dynamic synchronization in a brain-based device, Cerebral Cortex, Nov. 2004, pp. 1185-1199, vol. 14, No. 11.
Seth, A. K., et al., Texture discrimination by an autonomous mobile brain-based device with whiskers, IEEE Conference on Robotics and Automation, Apr. 2004, pp. 4925-4930, New Orleans, LA.
Seth, A. K., et al., Spatiotemporal processing of whisker input supports texture discrimination by a brain-based device, in Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior, eds. Schaal, et al., 2004, pp. 130-139, The MIT Press, Cambridge, MA.
Sporns, O. et al., Neuromodulation and plasticity in an autonomous robot, Neural Networks, 2002, pp. 761-774, vol. 15.
Uchibe, E., Competitive-cooperative-concurrent reinforcement learning with importance sampling, in Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior, eds. Schaal, S., et al., 2004, The MIT Press, Cambridge, MA.
Van Santen, J. P., et al. Elaborated Reichardt detectors, Journal of the Optical Society of America, Feb. 1985, pp. 300-321, vol. 2, No. 2.
Weng, J., Developmental robotics: theory and experiments, International Journal of Humanoid Robotics, 2004, pp. 199-236, vol. 1, No. 2.
Wolpert, D., et al., Internal models in the cerebellum, Trends in Cognitive Sciences, Sep. 1998, pp. 338-347, vol. 2, No. 9.
Wray, J., et al., A model of color vision based on cortical reentry, Cerebral Cortex, Sep./Oct. 2006, pp. 701-716, vol. 6.
Yamamoto, K., et al., Computational studies on acquisition and adaptation of ocular following responses based on cerebellar synaptic plasticity, Journal of Neurophysiology, Mar. 2002, pp. 1554-1571, vol. 87.
Zemel, R. S. et al., A model for encoding multiple object motions and self-motion in area MST of primate visual cortex, Journal of Neuroscience, Jan. 1, 1998, pp. 531-547, vol. 18, No. 1.
Search Report and Search Opinion dated Apr. 9, 2010 for European Application No. 06848895.6, 7 pages.
Krichmar, J.L. et al., Spatial navigation and causal analysis in a brain-based device modeling cortical-hippocampal interactions, Neuroinformatics, Sep. 1, 2005, vol. 3, No. 3, pp. 197-222.
Mehrtash, N. et al., Synaptic plasticity in spiking neural networks: a system approach, IEEE Transactions on Neural Networks, Sep. 1, 2003, vol. 14, No. 5, pp. 980-992.

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Brain-based device having a cerebellar model for predictive... does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Brain-based device having a cerebellar model for predictive..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Brain-based device having a cerebellar model for predictive... will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-4252451

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.