A neurodynamic account of spontaneous behaviour

PLoS Computational Biology

Volumn 7, Issue 10, 2011, Pages

  Namikawa, Jun ^a   Nishimoto, Ryunosuke ^a   Tani, Jun ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

MAMMALS; NEURAL NETWORK MODELS; STOCHASTIC SYSTEMS;

'CURRENT; ACTION SEQUENCES; DETERMINISTIC CHAOS; ENVIRONMENTAL INTERACTIONS; LEARN+; NEURODYNAMICS; PREFRONTAL CORTEX; PRIMITIVE ACTIONS; SELF-ORGANISED; STATISTICAL STRUCTURES;

DYNAMICS;

ARTICLE; BEHAVIORAL SCIENCE; BRAIN FUNCTION; CHAOS THEORY; CONTROLLED STUDY; IMAGERY; IMITATION; LEARNING ALGORITHM; MOLECULAR DYNAMICS; NERVE CELL NETWORK; NEUROPHYSIOLOGY; PREDICTION; PREFRONTAL CORTEX; PRIMARY MOTOR CORTEX; PROPRIOCEPTION; ROBOTICS; STOCHASTIC MODEL; SUPPLEMENTARY MOTOR AREA; ARTIFICIAL NEURAL NETWORK; BEHAVIOR; BIOLOGICAL MODEL; HUMAN; MOTOR CORTEX; PHYSIOLOGY; PSYCHOMOTOR PERFORMANCE;

PRIMATES;

BEHAVIOR; HUMANS; MODELS, NEUROLOGICAL; MOTOR CORTEX; NEURAL NETWORKS (COMPUTER); PREFRONTAL CORTEX; PSYCHOMOTOR PERFORMANCE; ROBOTICS;

EID: 80055087257     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1002221     Document Type: Article

References (62)

1. Gibson E, Pick A, (2000) An ecological approach to perceptual learning and development. Oxford University Press, USA.
2. Ito M, (1970) Neurophysiological aspects of the cerebellar motor control system. Int J Neurol 7: 162-176.
3. Uno Y, Kawato M, Suzuki R, (1989) Formation and control of optimal trajectory in human multi-joint arm movement. Biol Cybern 61: 89-101.
4. Wolpert D, Ghahramani Z, Jordan M, (1995) An internal model for sensorimotor integration. Science 269: 1880-1882.
5. McCarthy J, (1963) Situations, actions, and causal laws. Technical Report AIM-2, Artificial Intelligence Project, Stanford University.
6. Tani J, (2003) Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Netw 16: 11-23.
7. Ay N, Bertschinger N, Der R, Güttler F, Olbrich E, (2008) Predictive information and explorative behavior of autonomous robots. Eur Phys J B 63: 329-339.
8. Tani J, Ito M, Sugita Y, (2004) Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using rnnpb. Neural Netw 17: 1273-1289.
9. 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.
10. Jeannerod M, (1994) The representing brain: Neural correlates of motor intention and imagery. Behav Brain Sci 17: 187-202.
11. Friston K, Mattout J, Kilner J, (2011) Action understanding and active inference. Biol Cybern pp. 1-24.
12. Rao R, Ballard D, (1999) Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat Neurosci 2: 79-87.
13. Friston K, (2010) The free-energy principle: a unified brain theory? Nat Rev Neurosci 11: 127-138.
14. Evans G, (1981) Semantic theory and tacit knowledge. In: Holdzman S, Leich C, editors. Wittgenstein: To follow a rule 118 Oxford Routledge and Kegan Paul: 118-137.
15. Arbib M, (1981) Perceptual structures and distributed motor control. Handbook of Phys: The Nerv Syst II Motor Control pp. 1448-1480.
16. Sternad D, Schaal S, (1999) Segmentation of endpoint trajectories does not imply segmented control. Exp Brain Res 124: 118-136.
17. 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.
18. Saffran J, Aslin R, Newport E, (1996) Statistical learning by 8-month-old infants. Science 274: 1926-1928.
19. Kirkham N, Slemmer J, Johnson S, (2002) Visual statistical learning in infancy: Evidence for a domain general learning mechanism. Cognition 83: B35-B42.
20. Baldwin D, Andersson A, Saffran J, Meyer M, (2008) Segmenting dynamic human action via statistical structure. Cognition 106: 1382-1407.
21. Byrne R, (2003) Imitation as behaviour parsing. Philos T Roy Soc B 358: 529-536.
22. Ashley R, (2009) Musical improvisation. In: Hallam S, Cross I, Thaut M, editors. Oxford Handbook of Music Psychology Oxford Oxford University Press pp. 413-420.
23. Saffran J, Johnson E, Aslin R, Newport E, (1999) Statistical learning of tone sequences by human infants and adults. Cognition 70: 27-52.
24. Desain P, Honing H, Sadakata M, (2003) Predicting rhythm perception from rhythm production and score counts: The bayesian approach. In: Proceedings of the Society for Music Perception and Cognition 2003 Conference; 18 June 2003; Las Vegas, Nevada. SMPC 2003.
25. Nakamura K, Sakai K, Hikosaka O, (1998) Neuronal activity in medial frontal cortex during learning of sequential procedures. J Neurophysiol 80: 2671-2687.
26. Kennerley S, Sakai K, Rushworth M, (2004) Organization of action sequences and the role of the pre-sma. J Neurophysiol 91: 978-993.
27. Sakai K, Hikosaka O, Miyauchi S, Takino R, Tamada T, et al. (1999) Neural representation of a rhythm depends on its interval ratio. J Neurosci 19: 10074-10081.
28. Tanji J, Shima K, (1994) Role for supplementary motor area cells in planning several movements ahead. Nature 371: 413-416.
29. Shima K, Tanji J, (2000) Neuronal activity in the supplementary and presupplementary motor areas for temporal organization of multiple movements. J Neurophysiol 84: 2148-2160.
30. Namikawa J, Tani J, (2008) A model for learning to segment temporal sequences, utilizing a mixture of RNN experts together with adaptive variance. Neural Netw 21: 1466-1475.
31. Yamashita Y, Tani J, (2008) Emergence of functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Comput Biol 4: e1000220.
32. Sakai K, Hikosaka O, Nakamura K, (2004) Emergence of rhythm during motor learning. Trends Cogn Sci 8: 547-553.
33. Hume D, (1748/1977) An enquiry concerning human understanding Indianapolis Hackett Publishing.
34. James W, (1956) The Dilemma of Determinism, Reprinted in The Will to Believe Mineola Dover Publications.
35. Dennett D, (1984) Elbow room: The varieties of free will worth wanting The MIT Press.
36. Libet B, (1985) Unconscious cerebral initiative and the role of conscious will in voluntary action. Behav Brain Sci 8: 529-539.
37. Soon C, Brass M, Heinze H, Haynes J, (2008) Unconscious determinants of free decisions in the human brain. Nat Neurosci 11: 543-545.
38. Tsuda I, (1991) Chaotic itinerancy as a dynamical basis of hermeneutics in brain and mind. World Futures 32: 167-184.
39. Skarda C, Freeman W, (1987) How brains make chaos in order to make sense of the world. Behav Brain Sci 10: 161-173.
40. Tani J, (1998) An interpretation of the 'self' from the dynamical systems perspective: a constructivist approach. J Conscious Stud 5: 516-542.
41. Friston K, Kiebel S, (2009) Attractors in song. New Math Nat Comput 5: 83-114.
42. Kiebel S, Daunizeau J, Friston K, (2008) A hierarchy of time-scales and the brain. PLoS Comput Biol 4: e1000209.
43. Badre D, D'Esposito M, (2009) Is the rostro-caudal axis of the frontal lobe hierarchical? Nat Rev Neurosci 10: 659-669.
44. Mita A, Mushiake H, Shima K, Matsuzaka Y, Tanji J, (2009) Interval time coding by neurons in the presupplementary and supplementary motor areas. Nat Neurosci 12: 502-507.
45. Namikawa J, Tani J, (2010) Learning to imitate stochastic time series in a compositional way by chaos. Neural Netw 23: 625-638.
46. Jacobs R, Jordan M, Nowlan S, Hinton G, (1991) Adaptive mixtures of local experts. Neural Comput 3: 79-87.
47. Rumelhart D, Hintont G, Williams R, (1986) Learning representations by back-propagating errors. Nature 323: 533-536.
48. Fitzsimonds R, Song H, Poo M, (1997) Propagation of activity-dependent synaptic depression in simple neural networks. Nature 388: 439-448.
49. Du J, Poo M, (2004) Rapid bdnf-induced retrograde synaptic modification in a developing retinotectal system. Nature 429: 878-883.
50. Harris K, (2008) Stability of the fittest: organizing learning through retroaxonal signals. Trends Neurosci 31: 130-136.
51. Hoshi E, Shima K, Tanji J, (2000) Neuronal activity in the primate prefrontal cortex in the process of motor selection based on two behavioral rules. J Neurophysiol 83: 2355-2373.
52. Georgopoulos A, Kalaska J, Caminiti R, Massey J, (1982) On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci 2: 1527-1537.
53. Graziano M, Taylor C, Moore T, (2002) Complex movements evoked by microstimulation of pre-central cortex. Neuron 34: 841-851.
54. Wise S, Boussaoud D, Johnson P, Caminiti R, (1997) Premotor and parietal cortex: Corticocortical connectivity and combinatorial computations 1. Annu Rev Neurosci 20: 25-42.
55. Rizzolatti G, Luppino G, Matelli M, (1998) The organization of the cortical motor system: new concepts. Electroen Clin Neuro 106: 283-296.
56. Gros C, (2009) Cognitive computation with autonomously active neural networks: an emerging field. Cogn Comput 1: 77-90.
57. Markov A, (1971) Extension of the limit theorems of probability theory to a sum of variables connected in a chain. Dynam Probabilist Syst 1: 552-577.
58. Langevin P, (1908) On the theory of brownian motion. CR Acad Sci (Paris) 146: 530-533.
59. Grabert H, (1982) Projection operator techniques in nonequilibrium statistical mechanics Springer-Verlag.
60. Williams R, Zipser D, (1989) A learning algorithm for continually running fully recurrent neural networks. Neural Comput 1: 270-280.
61. Ball T, Schreiber A, Feige B, Wagner M, Lücking C, et al. (1999) The role of higher-order motor areas in voluntary movement as revealed by high-resolution EEG and fMRI. Neuroimage 10: 682-694.
62. Selemon L, Goldman-Rakic P, (1988) Common cortical and subcortical targets of the dorsolateral prefrontal and posterior parietal cortices in the rhesus monkey: evidence for a distributed neural network subserving spatially guided behavior. J Neurosci 8: 4049-4068.