Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong

Journal of Neurophysiology

Volumn 92, Issue 4, 2004, Pages 2274-2282

  Fiete, Ila R ^a,d,f   Hahnioser, Richard H R ^e   Fee, Michale S ^c,d   Seung, H Sebastian ^b,d  

^a HARVARD UNIVERSITY   (United States)

^b HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^e Institute of Neuroinformatics Zurich   (Switzerland)

^f UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIRDSONG; BRAIN NUCLEUS; CODING; DRIVE; HIGH VOCAL CENTER; JUVENILE; LEARNING; LEARNING SPEED; MODEL; MOTONEURON; MOTOR CORTEX; MOTOR LEARNING; MOTOR SYSTEM; NERVE CELL; NERVE CELL NETWORK; NERVE PROJECTION; NONHUMAN; PREMOTOR CORTEX; PRIORITY JOURNAL; ROBUSTUS ARCHISTRIATALIS NUCLEUS; SENSORIMOTOR FUNCTION; SENSORY CORTEX; SENSORY SYSTEM; SIMULATION; SINGING; TEMPORAL CODING; TIME; VELOCITY; VOCAL CORD;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BIRDS; BRAIN; COMPUTER SIMULATION; LINEAR MODELS; MODELS, NEUROLOGICAL; MOTOR NEURONS; MUSCLE, SKELETAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; VOCALIZATION, ANIMAL;

EID: 3242729839     PISSN: 00223077     EISSN: None     Source Type: Journal    
DOI: 10.1152/jn.01133.2003     Document Type: Article

References (35)

1. Bartlett P and Baxter J. Hebbian synaptic modifications in spiking neurons that learn. Australian National University, Canberra, 0200 Australia, 1999. Technical report.
2. Bottjer S, Miesner E, and Arnold A. Forebrain lesions disrupt development but not maintenance of song in passerine birds. Science 224: 901-903, 1984.
3. Brainard M and Doupe A. Auditory feedback in learning and maintenance of vocal behavior. Nat Rev Neurosci 1: 31-40, 2000.
4. Deweese M, Wehr M, and Zador A. Binary spiking in auditory cortex. J Neurosci 23: 7940-7949, 2003.
5. Doya K and Sejnowski T. A novel reinforcement model of birdsong vocalization learning. In: Advances in Neural Information Processing Systems 7, edited by Tesauro G, Touretzky D, and Leen T. Cambridge, MA: MIT Press, 1995, p. 101-108.
6. Edwards S and Jones R. The eigenvalue spectrum of a large symmetric random matrix. J Phys A 9: 1595, 1976.
7. Foldiak P. Sparse coding in the primate cortex. In: The Handbook of Brain Theory and Neural Networks, edited by Arbib M. Cambridge, MA: MIT Press, 1995, p. 895-898.
8. Hahnloser R, Kozhevnikov A, and Fee M. An ultra-sparse code underlies the generation of neural sequences in a songbird. Nature 419: 65-70, 2002.
9. Hermann M, Hertz J, and Prugel-Bennett A. Analysis of synfire chains. Network 6: 403-414, 1995.
10. Herrmann K and Arnold A. The development of afferent projections to the robust archistriatal nucleus in male zebra finches: a quantitative electron microscopic study. J Neurosci 11: 2063-2074, 1991.
11. Konishi M. The role of auditory feedback in the control of vocalization in the white-crowned sparrow. Z Tierpsychol 22: 770-783, 1965.
12. Le Cun Y, Kanter I, and Solla S. Eigenvalues of covariance matrices: application to neural learning. Phys Rev Lett 66: 2396-2399, 1991.
13. Lewicki M and Konishi M. Mechanisms underlying the sensitivity of songbird forebrain neurons to temporal order. Proc Natl Acad Sci USA 92: 5582-5586, 1995.
14. Margoliash D. Preference for autogenous song by auditory neurons in a song system nucleus of the white-crowned sparrow. J Neurosci 6: 1643-1661, 1986.
15. Margoliash D and Fortune E. Temporal and harmonic combination-sensitive neurons in the zebra finch's HVC. J Neurosci 12: 4309-4326, 1992.
16. Meunier C, Yanai H, and Amari S. Sparsely coded associative memories: capacity and dynamical properties. Netw Comput Neural Syst 2: 469-487, 1991.
17. Mooney R. Synaptic basis for developmental plasticity in a birdsong nucleus. J Neurosci 12: 2464-2477, 1992.
18. Nottebohm F, Kelley D, and Paton J. Connections of vocal control nuclei in the canary telencephalon. J Comp Neurol 207: 344-357, 1982.
19. Nottebohm F, Stokes T, and Leonard C. Central control of song in the canary, Serinus canarius. J Comp Neurol 165: 457-486, 1976.
20. Perez-Orive J, Mazor O, Turner G, Cassenaer S, Wilson R, and Laurent G. Oscillations and sparsening of odor representations in the mushroom body. Science 297: 359-365, 2002.
21. Sakaguchi H and Saito ND. Developmental changes in axon terminals visualized by immunofluorescence for the growth-associated protein, gap-43, in the robust nucleus of the archistriatum of the zebra finch. Brain Res Dev Brain Res 95: 245-251, 1996.
22. Seung H. Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron 40: 1063-1073, 2003.
23. Simpson H and Vicario D. Brain pathways for learned and unlearned vocalizations differ in zebra finches. J Neurosci 10: 1541-1556, 1990.
24. Spiro JE, Dalva MB, and Mooney R. Long-range inhibition within the zebra finch song nucleus RA. J Neurophysiol 81: 3007-3020, 1999.
25. Stark L and Perkel D. Two-stage, input-specific synaptic maturation in a nucleus essential for vocal production in the zebra finch. J Neurosci 19: 9107-9116, 1999.
26. Tchernichovski O, Mitra P, Lints T, and Nottebohm F. Dynamics of the vocal imitation process: how a zebra finch learns its song. Science 291: 2564-2569, 2001.
27. Theunissen F. From synchrony to sparseness. Trends Neurosci 26: 61-64, 2003.
28. Troyer T and Doupe A. An associational model of birdsong sensorimotor learning. I. Efference copy and the learning of song syllables. J Neurophysiol 84: 1204-1223, 2000.
29. Tsodyks M and Feigelman M. Enhanced storage capacity in neural networks with low level of activity. Europhys Lett 6: 101, 1988.
30. Turner R and Delong M. Corticostriatal activity in primary motor cortex of the macaque. J Neurosci 20: 7096-7108, 2000.
31. Volman S. Development of neural selectivity for birdsong during vocal learning. J Neurosci 13: 4737-4747, 1993.
32. Weliky M, Fiser J, Hunt R, and Wagner D. Coding of natural scenes in primary visual cortex. Neuron 37: 703-718, 2003.
33. Williams R. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning 8: 229-256, 1992.
34. Willshaw D, Buneman O, and Longuet-Higgins H. Non-holographic associative memory. Nature 222: 960-962, 1969.
35. Yu A and Margoliash D. Temporal hierarchical control of singing in birds. Science 273: 1871-1875, 1986.