Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1473-1481

  Pillow, Jonathan W ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

NEURAL NETWORKS; SIGNAL ENCODING;

ENCODING MODELS; HISTORY EFFECTS; NEURAL ENCODING; NEURAL RESPONSE; REAL- TIME; RENEWAL MODEL; RENEWAL PROCESS; RESCALING METHOD; STATISTIC MODELING; TIME-RESCALING;

MAXIMUM LIKELIHOOD ESTIMATION;

EID: 78650611326     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. E. Brown, R. Barbieri, V. Ventura, R. Kass, and L. Frank. The time-rescaling theorem and its application to neural spike train data analysis. Neural Computation, 14:325-346, 2002.
2. E. J. Chichilnisky. A simple white noise analysis of neuronal light responses. Network: Computation in Neural Systems, 12:199-213, 2001.
3. E. P. Simoncelli, L. Paninski, J. W. Pillow, and O. Schwartz. Characterization of neural responses with stochastic stimuli. In M. Gazzaniga, editor, The Cognitive Neurosciences, III, chapter 23, pages 327-338. MIT Press, 2004.
4. M. Berry and M. Meister. Refractoriness and neural precision. Journal of Neuroscience, 18:2200-2211, 1998.
5. Daniel S. Reich, Ferenc Mechler, Keith P. Purpura, and Jonathan D. Victor. Interspike intervals, receptive fields, and information encoding in primary visual cortex. J. Neurosci., 20(5):1964-1974, 2000.
6. N. Brenner, W. Bialek, and R. de Ruyter van Steveninck. Adaptive rescaling optimizes information transmission. Neuron, 26:695-702, 2000.
7. W. Gerstner. Population dynamics of spiking neurons: Fast transients, asynchronous states, and locking. Neural Computation, 12(1):43-89, 2000.
8. P. Reinagel and R. C. Reid. Temporal coding of visual information in the thalamus. Journal of Neuroscience, 20:5392-5400, 2000.
9. J. W. Pillow, L. Paninski, V. J. Uzzell, E. P. Simoncelli, and E. J. Chichilnisky. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. The Journal of Neuroscience, 25:11003-11013, 2005.
10. M.A. Montemurro, S. Panzeri, M. Maravall, A. Alenda, M.R. Bale, M. Brambilla, and R.S. Petersen. Role of precise spike timing in coding of dynamic vibrissa stimuli in somatosensory thalamus. Journal of Neurophysiology, 98(4):1871, 2007.
11. A.L. Jacobs, G. Fridman, R.M. Douglas, N.M. Alam, P. Latham, et al. Ruling out and ruling in neural codes. Proceedings of the National Academy of Sciences, 106(14):5936, 2009.
12. M. Berman. Inhomogeneous and modulated gamma processes. Biometrika, 68(1):143-152, 1981.
13. R. Barbieri, M.C. Quirk, L.M. Frank, M.A. Wilson, and E.N. Brown. Construction and analysis of non-poisson stimulus-response models of neural spiking activity. Journal of Neuroscience Methods, 105(1):25-37, 2001.
14. E. Rossoni and J. Feng. A nonparametric approach to extract information from interspike interval data. Journal of neuroscience methods, 150(1):30-40, 2006.
15. K. Koepsell and F.T. Sommer. Information transmission in oscillatory neural activity. Biological Cybernetics, 99(4):403-416, 2008.
16. R.E. Kass and V. Ventura. A spike-train probability model. Neural computation, 13(8):1713-1720, 2001.
17. 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.
18. J. W. Pillow, J. Shlens, L. Paninski, A. Sher, A. M. Litke, and E. P. Chichilnisky, E. J. Simoncelli. Spatiotemporal correlations and visual signaling in a complete neuronal population. Nature, 454:995-999, 2008.
19. S. Gerwinn, J.H. Macke, M. Seeger, and M. Bethge. Bayesian inference for spiking neuron models with a sparsity prior. Advances in Neural Information Processing Systems, 2008.
20. I.H. Stevenson, J.M. Rebesco, L.E. Miller, and K.P. Kording. Inferring functional connections between neurons. Current Opinion in Neurobiology, 18(6):582-588, 2008.
21. L. Paninski. Maximum likelihood estimation of cascade point-process neural encoding models. Network: Computation in Neural Systems, 15:243-262, 2004.
22. J. W. Pillow. Likelihood-based approaches to modeling the neural code. In K. Doya, S. Ishii, A. Pouget, and R. P. Rao, editors, Bayesian Brain: Probabilistic Approaches to Neural Coding, pages 53-70. MIT Press, 2007.
23. T.P. Coleman and S. Sarma. Using convex optimization for nonparametric statistical analysis of point processes. In IEEE International Symposium on Information Theory, 2007. ISIT 2007, pages 1476-1480, 2007.
24. L. Paninski, M. Fellows, S. Shoham, N. Hatsopoulos, and J. Donoghue. Superlinear population encoding of dynamic hand trajectory in primary motor cortex. J. Neurosci., 24:8551-8561, 2004.