Decoding spike train ensembles: Tracking a moving stimulus

Biological Cybernetics

Volumn 96, Issue 1, 2007, Pages 99-112

  Rossoni, Enrico ^a   Feng, Jianfeng ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DECODING; MAXIMUM LIKELIHOOD ESTIMATION; MOTION ESTIMATION; NEURAL NETWORKS; POPULATION STATISTICS; TRACKING (POSITION);

DYNAMICAL INPUT; SPATIAL INFORMATION; SPIKE ACTIVITY; TRAIN ENSEMBLES;

NEUROLOGY;

ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; BRAIN; PHYSIOLOGY;

BRAIN; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER);

EID: 33846869462     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-006-0106-4     Document Type: Article

References (23)

1. Amari S, Nakahara H (2005) Difficulty of singularity in population coding. Neural Comput 17:839-858
2. Andersen PK, Borgan O, Gill RD, Keiding N (1993) Statistical models based on counting processes. Springer, Berlin Heidelberg New York
3. Brown, EN, Kaas RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7:456-461
4. Feng JF (2004) Computational neuroscience: a comprehensive approach. Chapman & Hall/CRC, Boca Raton
5. Feng J, Ding M (2004) Decoding spikes in a spiking neuronal network. J Phys A Math Gen 37:5713-5727
6. Gerstner W, Kistler WM (2002) Spiking neuron models: single neurons, populations, plasticity. Cambridge University Press, Cambridge, UK
7. Gnedenko BV, Belyayev YK, Solovyev AD (1969) Mathematical methods of reliability theory. Academic, New York
8. Harris KD (2004) Hallucinations and nonsensory correlates of neural activity. Behav Brain Sci 27:796-796
9. Hollander M, Pena EA (2004) Nonparametric methods in reliability. Stat Sci 19:644-651
10. Horton P, Nicol A, Kendrick K, Feng JF (2005) Applications of multi-variate analysis of variance (MANOVA) to multielectrode array electrophysiology data. J Neurosci Methods 166:22-41
11. Klein JP, Moeschberger ML (1997) Survival analysis: techniques for censored and truncated data. Springer, Berlin Heidelberg New York
12. Paz R, Wise SP, Vaadia E (2004) Viewing and doing: similar cortical mechanisms for perceptual and motor learning. Trends Neurosci 27:496-503
13. Peng XM, Van Essen DC (2005) Peaked encoding of relative luminance in macaque areas V1 and V2. J Neurophysiol 93:1620-1632
14. Pettitt AN, Kelly JM, Gao JT (1998) Bias correction for censored data with exponential lifetimes. Statist Sinica 8:941-963
15. Raffi M, Siegel RM (2005) Functional architecture of spatial attention in the parietal cortex of the behaving monkey. J Neurosci 25:5171-5186
16. Rolls ET, Tovee MJ (1994) Processing speed in the cerebral cortex and the neurophysiology of visual masking. Proc R Soc Lond 257:9-15
17. Rossoni E, Feng J (2006) Non-parametric approach to extract information from interspike intervals. J Neurosci Methods 150:30-40
18. Sanger TD (2003) Neural population codes. Curr Opin Neurobiol 13:238-249
19. Schaette R, Gollisch T, Herz AVM (2005) Spike-train variability of auditory neurons in vivo: dynamic responses follow predictions from constant stimuli. J Neurophysiol 93:3270-3281
20. Shadlen MN, Newsome WT (1998) The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J Neurosci 18:3870-3896
21. Thorpe S, Fize D, Marlot C (1996) Speed of processing in the human visual system. Nature 381:520-522
22. Tuckwell HC (1988) Introduction to theoretical neurobiology: nonlinear and stochastic theories. Cambridge University Press, New York
23. Verbeek A (1989) The compactification of generalised linear models. In: Decorli A, Frances B, Gilchrist R and Seebeer G (eds) Statistical modelling. Springer, New York, pp 314-327