Self-Organized functional hierarchy through multiple timescales: Neuro-dynamical accounts for behavioral compositionality

Computational and Robotic Models of the Hierarchical Organization of Behavior

Volumn 9783642398759, Issue , 2013, Pages 47-62

  Yamashita, Yuichi ^a   Tani, Jun ^b  

^a NATIONAL INSTITUTE OF NEUROSCIENCE   (Japan)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN;

COMPOSITIONALITY; DYNAMIC ACTIONS; FUNCTIONAL HIERARCHY; MODELLING STUDIES; MULTIPLE TIME SCALE; NEURAL ACTIVITY; NEURAL DYNAMICS; NEURAL MODELLING; PHYSICAL ACTION; SELF-ORGANISED;

NEURONS;

EID: 84930518415     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-39875-9_3     Document Type: Chapter

References (46)

1. Arbib, M., Erdi, P., Szentagothai, J. (1998). Neural organization: structure, function, and dynamics. Cambridge: MIT.
2. Arie, H., Endo, T., Arakaki, T., Sugano, S., Tani, J. (2009). Creating novel goal-directed actions at criticality: A neuro-robotic experiment. New Mathematics and Natural Computation, 5, 307-334.
3. Baldassarre, G. (2002). A modular neural-network model of the basal ganglia's role in learning and selecting motor behaviours. Cognitive Systems Research, 3(1), 5-13.
4. Boemio, A., Fromm, S., Braun, A., Poeppel, D. (2005). Hierarchical and asymmetric temporal sensitivity in human auditory cortices. Nature Neuroscience, 8, 389-395.
5. Botvinick, M. (2008). Hierarchical models of behavior and prefrontal function. Trends in Cognitive Sciences, 12, 201-208.
6. Evans, G. (1981). chapter Semantic Theory and Tacit Knowledge. In Wittgenstein: to follow a rule (pp. 118-137). London: Routledge and Kegan Paul.
7. Felleman, D., & Van Essen, D. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1, 1-47.
8. Fuster, J. (2001). The prefrontal cortex-an update: time is of the essence. Neuron, 30, 319-333.
9. Giszter, S., Mussa-Ivaldi, F., Bizzi, E. (1993). Convergent force fields organized in the frog's spinal cord. Journal of Neuroscience, 13, 467-491.
10. Graziano, M., Taylor, C., Moore, T., Cooke, D. (2002). The cortical control of movement revisited. Neuron, 36, 349-362.
11. Haruno, M., Wolpert, D., Kawato, M. (2001). Mosaic model for sensorimotor learning and control. Neural Computation, 13, 2201-2220.
12. Hilgetag, C., O'Neill, M., Young, M. (2000). Hierarchical organization of macaque and cat cortical sensory systems explored with a novel network processor. Philosophical Transactions of the Royal Society of London B, 355, 71-89.
13. Honey, C., Kotter, R., Breakspear, M., Sporns, O. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences USA, 104, 10240-10245.
14. Hubener, M., Shoham, D., Grinvald, A., Bonhoeffer, T. (1997). Spatial relationships among three columnar systems in cat area 17. Journal of Neuroscience, 17, 9270-9284.
15. Huys, R., Daffertshofer, A., Beek, P. (2004). Multiple time scales and multiform dynamics in learning to juggle. Motor Control, 8, 188-212.
16. Ito, M., & Tani, J. (2004). On-line imitative interaction with a humanoid robot using a dynamic neural network model of a mirror system. Adaptive Behavior, 12, 93-115.
17. Jaeger, H., & Haas, H. (2004). Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 304, 78-80.
18. Kording, K., Tenenbaum, J., Shadmehr, R. (2007). The dynamics of memory as a consequence of optimal adaptation to a changing body. Nature Neuroscience, 10, 779-786.
19. Kuniyoshi, Y., & Sangawa, S. (2006). Early motor development from partially ordered neural-body dynamics - experiments with a cortico-spinal-musculo-skeletal model. Biological Cybernetics, 95, 589-605.
20. Maass, W., Natschlaeger, T., Markram, H. (2002). Real-time computing without stable states: s new framework for neural computation based on perturbations. Neural Computation, 14, 2531-2560.
21. Mussa-Ivaldi, F., & Bizzi, E. (2000). Motor learning through the combination of primitives. Philosophical Transactions of the Royal Society of London B, 355, 1755-1769.
22. Namikawa, J., Nishimoto, R., Tani, J. (2011). A neurodynamic account of spontaneous behaviour. PLoS Computational Biology, 7, e1002221.
23. Newell, K., Liu, Y., Mayer-Kress, G. (2001). Time scales in motor learning and development. Psychological Review, 108, 57-82.
24. Nishimoto, R., & Tani, J. (2009). Development of hierarchical structures for actions and motor imagery: A constructivist view from synthetic neuro-robotics study. Psychological Research, 73, 545-558.
25. 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. Connection Science, 14, 231-244.
26. Paine, R., & Tani, J. (2005). How hierarchical control self-organizes in artificial adaptive systems. Adaptive Behavior, 13, 211-225.
27. Poeppel, D., Idsardi, W., van Wassenhove, V. (2008). Speech perception at the interface of neurobiology and linguistics. Philosophical Transactions of the Royal Society of London B, Biology Science, 363, 1071-1086.
28. Precup, D., & Sutton, R. (1997). Multi-time models for temporally abstract planning. In Advances in neural information processing systems (vol. 10, pp. 1050-1056). Cambridge: MIT.
29. Sakai, K., Kitaguchi, K., Hikosaka, O. (2003). Chunking during human visuomotor sequence learning. Experimental Brain Research, 52, 229-242.
30. Schiller, P., & Logothetis, N. (1990). The color-opponent and broad-band channels of the primate visual system. Trends Neuroscience, 13, 392-398.
31. Seung, H. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40, 1063-1073.
32. Smith, M., Ghazizadeh, A., Shadmehr, R. (2006). Interacting adaptive processes with different timescales underlie short-term motor learning. PLoS Biology, 4, e179.
33. Sugita, Y., & Tani, J. (2004). Learning semantic combinatoriality from the interaction between linguistic and behavioral processes. Adaptive Behavior, 13, 33-52.
34. Tani, J. (2003). Learning to generate articulated behavior through the bottom-up and the top-down interaction processes. Neural Networks, 16, 11-23.
35. Tani, J., & Ito, M. (2003). Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment. IEEE Transactions on Systems, Man, and Cybernetics. Part A - Systems and Humans, 33, 481-488.
36. 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 Networks, 17, 1273-1289.
37. Tani, J., Nishimoto, R., Namikawa, J., Ito, M. (2008a). Codevelopmental learning between human and humanoid robot using a dynamic neural-network model. Systems, Man, and Cybernetics, Part B: Cybernetics, 38, 43-59.
38. Tani, J., Nishimoto, R., Paine, R. (2008b). Achieving 'organic compositionality' through selforganization: reviews on brain-inspired robotics experiments. Neural Networks, 21, 584-603.
39. Tani, J., & Nolfi, S. (1999). Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks, 12, 1131-1141.
40. Thoroughman, K., & Shadmehr, R. (2000). Learning of action through adaptive combination of motor primitives. Science, 407, 742-747.
41. Tokunaga, K., & Furukawa, T. (2009). Modular network som. Neural Networks, 22, 82-90.
42. Tootell, R., Silverman, M., De Valois, R. (1981). Spatial frequency columns in primary visual cortex. Science, 214, 813-815.
43. Varela, F., Lachaux, J., Rodriguez, E., Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2, 229-239.
44. Vuilleumier, P., Armony, J., Driver, J., Dolan, R. (2003). Distinct spatial frequency sensitivities for processing faces and emotional expressions. Nature Neuroscience, 6, 624-631.
45. Xie, X., & Seung, H. (2004). Learning in neural networks by reinforcement of irregular spiking. Physical Review E, 69, 041909.
46. Yamashita, Y., & Tani, J. (2008). Emergence of functional hierarchy in a multiple timescale neural network model: A humanoid robot experiment. PLoS Computational Biology, 4, e1000220.