Sensitivity derivatives for flexible sensorimotor learning

Neural Computation

Volumn 20, Issue 8, 2008, Pages 2085-2111

  Abdelghani, M N ^a   Lillicrap, T P ^b   Tweed, D B ^c,d  

^a UNIVERSITY OF TORONTO   (Canada)

^b QUEEN S UNIVERSITY   (Canada)

^c UNIVERSITY OF TORONTO   (Canada)

^d YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN; COMPUTER SIMULATION; FEEDBACK SYSTEM; LEARNING; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE;

ADAPTATION, PHYSIOLOGICAL; ALGORITHMS; BRAIN; COMPUTER SIMULATION; FEEDBACK; LEARNING; MOVEMENT; NEURAL NETWORKS (COMPUTER); PSYCHOMOTOR PERFORMANCE;

EID: 48249114761     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2008.04-07-507     Document Type: Article

References (34)

1. Ackley, D., Hinton, G. E., & Sejnowski, T. (1985). A learning algorithm for Boltzmann machines. Cog. Sci., 9, 147-169.
2. Åström, K. J., & Wittenmark, B. (1995). Adaptive control Reading, MA: Addison-Wesley.
3. Brinkman, C., Porter, R., & Norman, J. (1983). Plasticity of motor behavior in monkeys with crossed forelimb nerves. Science, 220, 438-440.
4. Callier, F. M., & Desoer, C. A. (1991) Linear system theory. New York: Springer.
5. Dean, P., Porrill, J., & Stone, J. V. (2002). Decorrelation control by the cerebellum achieves oculomotor plant compensation in simulated vestibulo-ocular reflex. Proc. R. Soc. B, 269, 1895-1904.
6. Ewert, P. (1930). A study of the effect of inverted retinal stimulation upon spatially coordinated behavior. Genet. Psychol. Monogr., 7, 177-363.
7. Forssberg, H., & Svatengren, G. (1983). Hardwired locomotor network in cat revealed by a retained motor pattern for gastrocnemius after muscle transposition. Neurosci. Lett., 41, 283-288.
8. Gardner, W. A. (1984). Learning characteristics of stochastic-gradient- descent algorithms: A general study, analysis, and critique. Signal Processing, 6, 113-133.
9. Haykin, S. (2002). Adaptive filter theory. Upper Saddle River, NJ: Prentice Hall.
10. Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Comput., 18, 1527-1554.
11. Jordan, M. I., & Rumelhart, D. E. (1992). Forward models: Supervised learning with a distal teacher. Cog. Sci., 16, 307-354.
12. Kawato, M., & Gomi, H. (1992). The cerebellum and VOR/OKR learning models. TINS, 15, 445-453.
13. Koch, C. (1999). Biophysics of computation. New York: Oxford University Press.
14. Leffert, R. D., & Meister, M. (1976). Patterns of neuromuscular activity following tendon transfer in the upper limb: A preliminary study. J. Hand Surg., 1, 181-189.
15. Lewis, F. L., Jagannathan, S., & Yeşildirek, A. (1999). Neural network control of robot manipulators and nonlinear systems. London: Taylor and Francis.
16. Mazzoni, P., Andersen, R. A., & Jordan, M. I. (1991). A more biologically plausible learning rule for neural networks. Proc. Natl Acad. Sci. USA, 88, 4433-4437.
17. Missiuro, W., & Kozlowski, S. (1963). Investigation on adaptive changes in reciprocal innervation of muscles. Arch. Phys. Med. Rehabil., 44, 37-41.
18. Nagumo, J., & Noda, A. (1967). A learning method for system identification. IEEE Trans. Automat Contr., AC-12, 283-287.
19. Oztas, E. (2003). Neuronal tracing. Neuroanatomy, 2, 2-5.
20. Porrill, J., Dean, P., & Stone, J. V. (2004). Recurrent cerebellar architecture solves the motor-error problem. Proc. R. Soc. B, 271, 789-796.
21. Rolls, E. T., & Deco, G. (2002). Computational neuroscience of vison. New York: Oxford University Press.
22. Shibata, T., Tabata, H., Schaal, S., & Kawato, M. (2005). A model of smooth pursuit in primates based on learning the target dynamics. Neural Networks, 18(3), 213-224.
23. Sperry, R. W. (1943). Effect of 180 degree rotation of the retinal field on visuomotor coordination. J. Exp. Zool., 92, 263-279.
24. Sperry, R. W. (1945). The problem of central nervous reorganization after nerve regeneration and muscle transposition. Q. Rev. Biol., 20(4), 311-369.
25. Stratton, G. M. (1897). Vision without inversion of the retinal image. Psychol. Rev., 4, 341-360, 463-481.
26. Sugita, Y. (1996). Global plasticity in adult visual cortex following reversal of visual input. Nature, 380, 523-526.
27. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning. Cambridge, MA: MIT Press.
28. Todorov, E., & Jordan, M. I. (2002). Optimal feedback control as a theory of motor coordination. Nat. Neurosci., 5, 1226-1235.
29. Tripp, B. Y., & Eliasmith, C. (2006). Comparison of neural circuits that estimate temporal derivatives. COSYNE 2006 abstracts. Available online at http://cosyne.org/program06/cosyne06_abstractbook_final.pdf.
30. Tweed, D., Misslisch, H., & Fetter, M. (1994). Testing models of the oculomotor velocity-to-position transformation. J. Neurophysiol., 72, 1425-1429.
31. Vera, C. L., Lewin, M. G., Kasa, J. C., & Calderon, M. T. D. (1975). Central functional changes after facial-spinal-accessory anastomosis in man and facial-hypoglossal anastomosis in the cat. J. Neurosurg., 43, 181-191.
32. Werfel, J., Xie, X., & Seung, H. S. (2005). Learning curves for stochastic gradient descent in linear feedforward networks. Neural Comput., 17, 2699-2718.
33. Yamamoto, K., Kobayashi, Y., Takemura, A., Kawano, K., & Kawato, M. (2002). Computational studies on acquisition and adaptation of ocular following responses based on cerebellar synaptic plasticity. J. Neurophysiol., 87, 1554-1571.
34. Yumiya, H., Larsen, K. D., & Asanuma, H. (1979). Motor readjustment and input-output relationship of motor cortex following crossconnection of forearm muscles in cats. Brain Res., 177, 566-570.