Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment

PLoS Computational Biology

Volumn 4, Issue 11, 2008, Pages

  Yamashita, Yuichi ^a   Tani, Jun ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; NEURAL NETWORK MODELS; NEURONS;

FUNCTIONAL HIERARCHY; HIERARCHICAL STRUCTURES; HUMAN BEING; HUMANOID ROBOT; MOTOR CONTROL SYSTEM; MOTOR PRIMITIVES; MULTIPLE TIME SCALE; NEURAL MECHANISMS; NEURAL NETWORK MODEL; SEQUENCE PATTERNS;

ANTHROPOMORPHIC ROBOTS;

ARTICLE; FEEDBACK SYSTEM; NERVE CELL NETWORK; ROBOTICS; SENSORIMOTOR FUNCTION; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; CENTRAL NERVOUS SYSTEM FUNCTION; HUMAN; LEARNING; METHODOLOGY; NERVE CELL; NERVE CELL PLASTICITY; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE; SYNAPSE; SYSTEMS BIOLOGY; TIME;

GENERALIZATION, STIMULUS; HIGHER NERVOUS ACTIVITY; HUMANS; MODELS, NEUROLOGICAL; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURONAL PLASTICITY; NEURONS; PSYCHOMOTOR PERFORMANCE; ROBOTICS; SYNAPSES; SYSTEMS BIOLOGY; TIME FACTORS;

EID: 57149090913     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1000220     Document Type: Article

References (67)

1. Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate cerebral cortex. Cereb Cortex 1: 1-47.
2. Hilgetag CC, O'Neill MA, Young MP (2000) Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor. Philos Trans R Soc Lond B Biol Sci 355: 71-89.
3. Fuster JM (2001) The prefrontal cortex-an update: time is of the essence. Neuron 30: 319-333.
4. Boemio A, Fromm S, Braun A, Poeppel D (2005) Hierarchical and asymmetric temporal sensitivity in human auditory cortices. Nat Neurosci 8: 389-395.
5. Arbib MA, Erdi P, Szentagothai J (1998) Neural Organization: Structure, Function, and Dynamics. Cambridge (Massachusetts): MIT Press.
6. Mussa-Ivaldi FA, Bizzi E (2000) Motor learning through the combination of primitives. Philos Trans R Soc Lond B Biol Sci 355: 1755-1769.
7. Kuniyoshi Y, Sangawa S (2006) Early motor development from partially ordered neural-body dynamics-experiments with a cortico-spinal-musculo-skeletal model. Biol Cybern 95: 589-605.
8. Thoroughman KA, Shadmehr R (2000) Learning of action through adaptive combination of motor primitives. Science 407: 742-747.
9. Sakai K, Kitaguchi K, Hikosaka O (2003) Chunking during human visuomotor sequence learning. Exp Brain Res 152: 229-242.
10. Giszter SF, Mussa-Ivaldi FA, Bizzi E (1993) Convergent force fields organized in the frog's spinal cord. J Neurosci 13: 467-491.
11. Graziano MS, Taylor CS, Moore T, Cooke DF (2002) The cortical control of movement revisited. Neuron 36: 349-362.
12. Varela FJ, Thompson E, Rosch E (1991) The Embodied Mind. Cambridge (Massachusetts): MIT Press.
13. Pfeifer R, Lungarella M, Iida F (2007) Self-organization, embodiment, and biologically inspired robotics. Science 318: 1088-1093.
14. Doya K (2001) Robotic neuroscience: a synthetic approach to the brain. Neurosci Res Suppl 24: S16.
15. Tani J, Nishimoto R, Paine R (2008) Achieving "organic compositionality" through self-organization: reviews on brain-inspired robotics experiments. Neural Netw 21: 584-603.
16. Tani J, Nolfi S (1999) Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems. Neural Netw 12: 1131-1141.
17. Haruno M, Wolpert DM, Kawato M (2001) Mosaic model for sensorimotor learning and control. Neural Comput 13: 2201-2220.
18. Tani J, Nishimoto R, Namikawa J, Ito M (2008) Codevelopmental learning between human and humanoid robot using a dynamic neural-network model. IEEE Trans Syst Man Cybern B Cybern 38: 43-59.
19. Newell KM, Liu YT, Mayer-Kress G (2001) Time scales in motor learning and development. Psychol Rev 108: 57-82.
20. Huys R, Daffertshofer A, Beek PJ (2004) Multiple time scales and multiform dynamics in learning to juggle. Motor Control 8: 188-212.
21. Smith MA, Ghazizadeh A, Shadmehr R (2006) Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biol 4: e179. doi:10.1371/journal.pbio.0040179.
22. Kording KP, Tenenbaum JB, Shadmehr R (2007) The dynamics of memory as a consequence of optimal adaptation to a changing body. Nat Neurosci 10: 779-786.
23. Varela F, Lachaux JP, Rodriguez E, Martinerie J (2001) The brainweb: phase synchronization and large-scale integration. Nat Rev Neurosci 2: 229-239.
24. Honey CJ, Kötter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci U S A 104: 10240-10245.
25. Poeppel D, Idsardi WJ, van Wassenhove V (2008) Speech perception at the interface of neurobiology and linguistics. Philos Trans R Soc Lond B Biol Sci 363: 1071-1086.
26. Nolfi S (2002) Evolving robots able to self-localize in the environment: the importance of viewing cognition as the result of processes occurring at different time scales. Connect Sci 14: 231-244.
27. Paine PW, Tani J (2005) How hierarchical control self-organizes in artificial adaptive systems. Adapt Behav 13: 211-225.
28. Thelen E, Schöner G, Scheier C, Smith LB (2001) The dynamics of embodiment: a field theory of infant perseverative reaching. Behav Brain Sci 24: 1-34.
29. Friston KJ (2005) A theory of cortical responses. Philos Trans R Soc Lond B Biol Sci 360: 815-836.
30. Grimes DB, Rashid DR, Rao RP (2006) Learning nonparametric models for probabilistic imitation. Adv Neural Inf Process Syst 19: 521-528.
31. Kemere C, Santhanam G, Yu BM, Afshar A, Ryu SI, Meng TH, Shenoy KV (2008) Detecting neural state transitions using hidden Markov models for motor cortical prostheses. J Neurophysiol, In press.
32. Li M, Greenside H (1996) Stable propagation of a burst through a one-dimensional homogeneous excitatory chain model of songbird nucleus HVC. Phys Rev E 74: 011918.
33. Fiete IR, Fee MS, Seung HS (2007) Model of birdsong learning based on gradient estimation by dynamic perturbation of neural conductances. J Neurophysiol 98: 2038-2057.
34. Kang S, Kitano K, Fukai T (2008) Structure of spontaneous UP and DOWN transitions self-organizing in a cortical network model. PLoS Comput Biol 4: e1000022. doi:10.1371/journal.pcbi.1000022.
35. Elman J (1990) Finding structure in time. Cogn Sci 14: 179-211.
36. Jordan MI, Rumelhart DE (1992) Forward models: supervised learning with a distal teacher. Cogn Sci 16: 307-354.
37. Fetz EE, Shupe LE (2002) Recurrent network: neurophysiological modeling. In: The Hand Book of Brain Theory and NeuralNnetwork. Cambridge (Massachusetts): MIT Press.
38. Doya K, Yoshizawa S (1989) Adaptive neural oscillator using continuous-time back-propagation learning. Neural Netw 2: 375-386.
39. Nishimoto R, Namikawa J, Tani J (2008) Learning multiple goal-directed actions through self-organization of a dynamic neural network model: a humanoid robot experiment. Adapt Behav 16: 166-181.
40. Wolpert DM, Ghahramani Z, Jordan MI (1995) An internal model for sensorimotor integration. Science 269: 1880-1882.
41. Wolpert D, Kawato M (1998) Multiple paired forward and inverse models for motor control. Neural Netw 11: 1317-1329.
42. Shibata T, Tabata H, Schaal S, Kawato M (2005) A model of smooth pursuit in primates based on learning the target dynamics. Neural Netw 18: 213-224.
43. Mulliken GH, Musallam S, Andersen RA (2008) Forward estimation of movement state in posterior parietal cortex. Proc Natl Acad Sci U S A 105: 8170-8177.
44. Nishimoto R, Tani J (2004) Learning to generate combinatorial action sequences utilizing the initial sensitivity of deterministic dynamical systems. Neural Netw 17: 925-933.
45. Rumelhart DE, McClelland JL (1986) Parallel distributed processing. Cambridge: MIT Press.
46. Seung HS (2003) Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40: 1063-1073.
47. Xie X, Seung HS (2004) Learning in neural networks by reinforcement of irregular spiking. Phys Rev E Stat Nonlin Soft Matter Phys 69: 041909.
48. Jeannerod M (1994) The representing brain: neural correlates of motor imitation and imaginary. Behav Brain Sci 17: 187-245.
49. Tani J (1996) Model-based learning for mobile robot navigation from the dynamical systems perspective. IEEE Trans Syst Man Cybern B Cybern 26: 421-436.
50. Sporns O, Honey CJ, Kötter R (2007) Identification and classification of hubs in brain networks. PLoS ONE 2: e1049. doi:10.1371/journal.pone.0001049.
51. Honey CJ, Sporns O (2008) Dynamical consequences of lesions in cortical networks. Hum Brain Mapp 29: 802-809.
52. Rizzolatti G, Luppino G (2001) The cortical motor system. Neuron 31: 889-901.
53. Dum RP, Strick PL (2002) Motor areas in the frontal lobe of the primate. Physiol Behav 77: 677-682.
54. Asanuma H, Rosén I (1972) Topographical organization of cortical efferent zones projecting to distal forelimb muscles in the monkey. Exp Brain Res 14: 243-256.
55. Kakei S, Hoffman D, Strick P (1999) Muscle and movement representations in the primary motor cortex. Science 285: 2136-2139.
56. Weinrich M, Wise SP (1982) The premotor cortex of the monkey. J Neurosci 2: 1329-1345.
57. Weinrich M, Wise SP, Mauritz KH (1984) A neurophysiological study of the premotor cortex in the rhesus monkey. Brain 107: 385-414.
58. Rizzolatti G, Camarda R, Fogassi M, Gentilucci M, Luppino G, Matelli M (1988) Functional organization of inferior area 6 in macaque monkey: II. Area F5 and the control of distal movements. Exp Brain Res 71: 491-507.
59. Cisek P, Kalaska JF (2005) Neural correlates of reaching decisions in dorsal premotor cortex: specification of multiple direction choices and final selection of action. Neuron 45: 801-814.
60. Hoshi E, Tanji J (2007) Distinctions between dorsal and ventral premotor areas: anatomical connectivity and functional properties. Curr Opin Neurobiol 17: 234-242.
61. Mushiake H, Inase M, Tanji J (190) Selective coding of motor sequence in the supplementary motor area of the monkey cerebral cortex. Exp Brain Res 82: 208-210.
62. Graziano MS, Aflalo TN (2007) Mapping behavioral repertoire onto the cortex. Neuron 56: 239-251.
63. Tootell RB, Silverman MS, De Valois RL (1981) Spatial frequency columns in primary visual cortex. Science 214: 813-815.
64. Hubener M, Shoham D, Grinvald A, Bonhoeffer T (1997) Spatial relationships among three columnar systems in cat area 17. J Neurosci 17: 9270-9284.
65. Vuilleumier P, Armony JL, Driver J, Dolan RJ (2003) Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nat Neurosci 6: 624-631.
66. Schiller PH, Logothetis NK (1990) The color-opponent and broad-band channels of the primate visual system. Trends Neurosci 13: 392-398.
67. Kohonen T (1996) Self-Organizing Maps. Berlin: Springer.