Bayesian spike-triggered covariance analysis

Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, NIPS 2011

Volumn , Issue , 2011, Pages

  Park, Il Memming ^a   Pillow, Jonathan W ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAXIMUM LIKELIHOOD ESTIMATION;

BAYESIAN; COVARIANCE ANALYSIS; FEATURE SPACE; GAUSSIANS; GENERALISATION; HIGH DIMENSIONAL SPACES; LOG LIKELIHOOD; MODEL-BASED OPC; MULTI DIMENSIONAL; POISSON MODEL;

BAYESIAN NETWORKS;

EID: 85162510325     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. J. Bussgang. Crosscorrelation functions of amplitude-distorted gaussian signals. RLE Technical Reports, 216, 1952.
2. E. J. Chichilnisky. A simple white noise analysis of neuronal light responses. Network: Comput. Neural Syst., 12:199-213, 2001.
3. R. de Ruyter and W. Bialek. Real-time performance of a movement-senstivive neuron in the blowfly visual system. Proc. R. Soc. Lond. B, 234:379-414, 1988.
4. O. Schwartz, E. J. Chichilnisky, and E. P. Simoncelli. Characterizing neural gain control using spiketriggered covariance. Adv. Neural Information Processing Systems, pages 269-276, 2002.
5. O. Schwartz, J. W. Pillow, N. C. Rust, and E. P. Simoncelli. Spike-triggered neural characterization. J. Vision, 6(4):484-507, 7 2006.
6. E. P. Simoncelli, J. Pillow, L. Paninski, and O. Schwartz. Characterization of neural responses with stochastic stimuli. The Cognitive Neurosciences, III, chapter 23, pages 327-338. MIT Press, 2004.
7. S. Gerwinn, J. Macke, M. Seeger, and M. Bethge. Bayesian inference for spiking neuron models with a sparsity prior. Adv. in Neural Information Processing Systems 20, pages 529-536. MIT Press, 2008.
8. L. Paninski. Convergence properties of some spike-triggered analysis techniques. Network: Comput. Neural Syst., 14:437-464, 2003.
9. J. W. Pillow and E. P. Simoncelli. Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis. J. Vision, 6(4):414-428, 4 2006.
10. C. M. Bishop. Bayesian PCA. Adv. in Neural Information Processing Systems, pages 382-388, 1999.
11. M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. J. the Royal Statistical Society. Series B, Statistical Methodology, pages 611-622, 1999.
12. T. P. Minka. Automatic choice of dimensionality for PCA. NIPS, pages 598-604, 2001.
13. M. Welling, F. Agakov, and C. K. I. Williams. Extreme components analysis. Adv. in Neural Information Processing Systems 16. MIT Press, 2004.
14. Y. Chen and M. Welling. Bayesian extreme components analysis. IJCAI, 2009.
15. T. Sharpee, N. C. Rust, and W. Bialek. Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput, 16(2):223-250, Feb 2004.
16. N. C. Rust, O. Schwartz, J. A. Movshon, and E. P. Simoncelli. Spatiotemporal elements of macaque V1 receptive fields. Neuron, 46(6):945-956, Jun 2005.
17. L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models. Network: Comput. Neural Syst., 15(04):243-262, November 2004.
18. N. Brenner, S. P. Strong, R. Koberle, W. Bialek, and R. R. de Ruyter van Steveninck. Synergy in a neural code. Neural Comput, 12(7):1531-1552, Jul 2000.
19. L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems, 15:243-262, 2004.
20. F. Theunissen, S. David, N. Singh, A. Hsu,W. Vinje, and J. Gallant. Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network: Comput. Neural Syst., 12:289-316, 2001.
21. M. Sahani and J. Linden. Evidence optimization techniques for estimating stimulus-response functions. NIPS, 15, 2003.
22. S. V. David, N. Mesgarani, and S. A. Shamma. Estimating sparse spectro-temporal receptive fields with natural stimuli. Network: Comput. Neural Syst., 18(3):191-212, 2007.
23. I. H. Stevenson, J. M. Rebesco, N. G. Hatsopoulos, Z. Haga, L. E. Miller, and K. P. Körding. Bayesian inference of functional connectivity and network structure from spikes. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(3):203-213, 2009.
24. S. Gerwinn, J. H Macke, and M. Bethge. Bayesian inference for generalized linear models for spiking neurons. Frontiers in Computational Neuroscience, 2010.
25. A. Calabrese, J. W. Schumacher, D. M. Schneider, L. Paninski, and S. M. N. Woolley. A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds. PLoS One, 6(1):e16104, 2011.
26. W. James and C. Stein. Estimation with quadratic loss. 4th Berkeley Symposium on Mathematical Statistics and Probability, 1:361-379, 1960.
27. M. Tipping. Sparse Bayesian learning and the relevance vector machine. JMLR, 1:211-244, 2001.
28. D. Donoho and M. Elad. Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. PNAS, 100:2197-2202, 2003.
29. K. R. Rad and L. Paninski. Efficient, adaptive estimation of two-dimensional firing rate surfaces via gaussian process methods. Network: Comput. Neural Syst., 21(3-4):142-168, 2010.
30. D.Wipf and S. Nagarajan. A new view of automatic relevance determination. Adv. in Neural Information Processing Systems 20, pages 1625-1632. MIT Press, 2008.
31. W. Truccolo, U. T. Eden, M. R. Fellows, J. P. Donoghue, and E. N. Brown. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. J. Neurophysiol, 93(2):1074-1089, 2005.