Developmental learning of complex syntactical song in the Bengalese finch: A neural network model

Neural Networks

Volumn 21, Issue 9, 2008, Pages 1224-1231

  Yamashita, Yuichi ^a   Takahasi, Miki ^a   Okumura, Tetsu ^a   Ikebuchi, Maki ^a   Yamada, Hiroko ^a   Suzuki, Madoka ^a   Okanoya, Kazuo ^a   Tani, Jun ^a  

^a RIKEN BRAIN SCIENCE INSTITUTE   (Japan)

Author keywords

Birdsong; Development; HVC; NIf; Noise; Recurrent neural network; Zebra finch

Indexed keywords

FLOW INTERACTIONS; IMAGE CLASSIFICATION; NETWORK PROTOCOLS; RECURRENT NEURAL NETWORKS; REINFORCEMENT LEARNING; SENSOR NETWORKS;

BIRDSONG; DEVELOPMENT; HVC; NIF; NOISE; ZEBRA FINCH;

NEURAL NETWORKS;

ANIMAL EXPERIMENT; ARTICLE; ARTIFICIAL NEURAL NETWORK; DEVELOPMENT; FINCH; LEARNING; MATHEMATICAL MODEL; NONHUMAN; PRIORITY JOURNAL; SIMULATION; VOCALIZATION;

AGING; ALGORITHMS; ANIMALS; BRAIN; FEEDBACK, PSYCHOLOGICAL; FINCHES; LEARNING; NEURAL NETWORKS (COMPUTER); VOCALIZATION, ANIMAL;

EID: 54449083467     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2008.03.003     Document Type: Article

References (19)

1. Cover T.M., and Thomas J.A. Element of information theory (1991), Wiley, New York
2. Doya K., and Sejnowski T.J. A novel reinforcement model of birdsong vocalization learning. Advances in Neural Information Processing Systems 7 (1995) 101-108
3. Elman J. Finding structure in time. Cognitive Science 14 (1990) 179-211
4. Fee M.S., Kozhevnikov A.A., and Hahnloser R.H. Neural mechanisms of vocal sequence generation in the songbird. Annals of the New York Academy of Sciences 1016 (2004) 153-170
5. Fetz E.E., and Shupe L.E. Recurrent network: Neurophysiological modeling. The hand book of brain theory and neural network (2002), MIT Press, Cambridge
6. Fiete I.R., Hahnloser R.H., Fee M.S., and Seung H.S. Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong. Journal of Neurophysiology 92 (2004) 2274-2282
7. Funabiki Y., and Konishi M. Long memory in song learning by zebra finches. Journal of Neuroscience 23 (2003) 6928-6935
8. Honda E., and Okanoya K. Acoustical and syntactical comparison between songs of the white-backed Munia (Lonchura striata) and its Domesticated strain, the Bengalese finch (Lonchura striata var.domestica). Zoological Science l16 (1999) 319-326
9. Hosino T., and Okanoya K. Lesion of a higher-order song nucleus disrupts phrase level complexity in Bengalese finches. Neuroreport 11 (2000) 2091-2095
10. Hoshino, C., & Doya, K. (2000). learning of hierarchical temporal sequences in biological systems. Technical report of IEICE, NC99-108. pp. 117-124
11. Jordan M.I., and Rumelhart D.E. Forward models: Supervised learning with a distal teacher. Cognitive Science 16 (1992) 307-354
12. Katahira K., Okanoya K., and Okada M. A neural network model for generating complex birdsong syntax. Biological Cybernetics 97 (2007) 441-448
13. McCasland J.S. Neuronal control of bird song production. Journal of Neuroscience 7 (1987) 23-39
14. Nottebohm F. The neural basis of birdsong. PLoS Biology l3 (2005) 759-761
15. Nottebohm F., Stokes T.M., and Leonard C.M. Central control of song in the canary, Serin us canaries. The Journal of Comparative Neurology 165 (1976) 457-486
16. Ölveczky B.P., Andalman A.S., and Fee M.S. Vocal experimentation in the juvenile songbird requires a basal ganglia circuit. PLoS Biology l3 (2005) 902-909
17. Rumelhart D.E., and McClelland J.L. Parallel distributed processing (1986), MIT Press, Cambridge
18. Vu E.T., Mazurek M.E., and Kuo Y.C. Identification of a forebrain motor programming network for the learned song of zebra finches. Journal of Neuroscience 14 (1994) 6924-6934
19. Zann R.A. The zebra finch (1996), Oxford University Press, Oxford