Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods

Journal of Computational Neuroscience

Volumn 33, Issue 1, 2012, Pages 1-19

  Paninski, Liam ^a   Vidne, Michael ^a   DePasquale, Brian ^a   Gil Ferreira, Daniel ^a  

^a Columbia University ^*  (United States)

Author keywords

Generalized linear model; Particle filter; Synaptic filtering

Indexed keywords

BANDPASS FILTERS; CONVEX OPTIMIZATION; MAXIMUM PRINCIPLE; MONTE CARLO METHODS; TIMING CIRCUITS;

CURRENT CLAMPS; GENERALIZED LINEAR MODEL; PARTICLE FILTER; POSTSYNAPTIC CELLS; SEQUENTIAL MONTE CARLO METHODS; SYNAPTIC FILTERING; SYNAPTIC INPUT; TIME COURSE; VOLTAGE CLAMP; VOLTAGE CURRENT;

RECOVERY;

ALGORITHM; ARTICLE; EXCITATORY POSTSYNAPTIC POTENTIAL; INHIBITORY POSTSYNAPTIC POTENTIAL; MONTE CARLO METHOD; NERVE CELL MEMBRANE CONDUCTANCE; NERVE CELL MEMBRANE STEADY POTENTIAL; NONPARAMETRIC TEST; PARAMETRIC TEST; PRIORITY JOURNAL; PROCESS OPTIMIZATION; SENSORY STIMULATION; SIGNAL NOISE RATIO; STATISTICAL MODEL;

EID: 84863874478     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-011-0371-7     Document Type: Article

References (51)

1. Ahmadian, Y., Packer, A., Yuste, R., & Paninski, L. (2011). Designing optimal stimuli to control neuronal spike timing. Journal of Neurophysiology, 106, 1038-1053.
2. Anderson, J., Carandini, M., & Ferster, D. (2000). Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. Journal of Neurophysiology, 84(2), 909.
3. Araya, R., Jiang, J., Eisenthal, K. B., & Yuste, R. (2006). The spine neck filters membrane potentials. PNAS, 103(47), 17961-17966. (Pubitemid 44852268)
4. Badel, L., Richardson, M., & Gerstner, W. (2005). Dependence of the spike-triggered average voltage on membrane response properties. Neurocomputing, 69, 1062-1065.
5. Bickel, P., Li, B., & Bengtsson, T. (2008). Sharp failure rates for the bootstrap particle filter in high dimensions. IMS Collections 2008 (Vol. 3, pp. 318-329).
6. Borg-Graham, L., Monier, C., & Fregnac, Y. (1996). Voltage-clamp measurement of visually-evoked conductances with whole-cell patch recordings in primary visual cortex. Journal of Physiology-Paris, 90(3-4), 185-188. (Pubitemid 27043061)
7. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Oxford University Press.
8. Brette, R., Piwkowska, Z., Rudolph, M., Bal, T., & Destexhe, A. (2007). A nonparametric electrode model for intracellular recording. Neurocomputing, 70, 1597-1601. (Pubitemid 46671090)
9. Brockwell, A., Rojas, A., & Kass, R. (2004). Recursive Bayesian decoding of motor cortical signals by particle filtering. Journal of Neurophysiology, 91, 1899-1907. (Pubitemid 38339355)
10. Cafaro, J., & Rieke, F. (2010). Noise correlations improve response fidelity and stimulus encoding. Nature, 468(7326), 964-967.
11. Casella, G., & Berger, R. (2001). Statistical inference. Duxbury Press.
12. Cocco, S., Leibler, S., & Monasson, R. (2009). Neuronal couplings between retinal ganglion cells inferred by efficient inverse statistical physics methods. Proceedings of the National Academy of Sciences, 106(33), 14058-14062.
13. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39, 1-38.
14. Dombeck, D., Blanchard-Desce, M., & Webb, W. (2004). Optical recording of action potentials with second-harmonic generation microscopy. The Journal of Neuroscience, 24(4), 999.
15. Douc, R., Cappe, O., & Moulines, E. (2005). Comparison of resampling schemes for particle filtering. In Proc. 4th int. symp. image and signal processing and analyis.
16. Doucet, A., De Freitas, N., Murphy, K., & Russell, S. (2000). Rao-Blackwellised particle filtering for dynamic bayesian networks. In Proceedings of the sixteenth conference on uncertainty in artif icial intelligence (pp. 176-183). Citeseer.
17. Doucet, A., De Freitas, N., & Gordon, N. (2001). Sequential Monte Carlo methods in practice. Springer.
18. Ergun, A., Barbieri, R., Eden, U., Wilson, M., & Brown, E. (2007). Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods. IEEE Transactions on Biomedical Engineering, 54, 419-428. (Pubitemid 46371228)
19. Gelman, A., Carlin, J., Stern, H., & Rubin, D. (2003). Bayesian data analysis. CRC Press.
20. Godsill, S., Doucet, A., &West, M. (2004).Monte Carlo smoothing for non-linear time series. Journal of the American Statistical Association, 99, 156-168. (Pubitemid 38545203)
21. Green, P., & Silverman, B. (1994). Nonparametric regression and generalized linear models. CRC Press.
22. Huys, Q., & Paninski, L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLOS Computational Biology, 5, e1000379.
23. Huys, Q., Ahrens, M., & Paninski, L. (2006). Efficient estimation of detailed single-neuron models. Journal of Neurophysiology, 96, 872-890. (Pubitemid 44159755)
24. Kelly, R., & Lee, T. (2004). Decoding V1 neuronal activity using particle filtering with Volterra kernels. Advances in Neural Information Processing Systems, 15, 1359-1366.
25. Koch, C. (1999). Biophysics of computation. Oxford University Press.
26. Kotecha, J. H., & Djuric, P.M. (2003). Gaussian particle filtering. IEEE Transactions on Signal Processing, 51, 2592-2601.
27. Koyama, S., & Paninski, L. (2010). Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models. Journal of Computational Neuroscience, 29(1), 89-105.
28. McCullagh, P., & Nelder, J. (1989). Generalized linear models. London: Chapman and Hall.
29. Murphy, G. & Rieke, F. (2006). Network variability limits stimulus-evoked spike timing precision in retinal ganglion cells. Neuron, 52, 511-524. (Pubitemid 44667433)
30. Nuriya, M., Jiang, J., Nemet, B., Eisenthal,K.,&Yuste,R. (2006). Imaging membrane potential in dendritic spines. PNAS, 103, 786-790. (Pubitemid 43153103)
31. Olsen, R. J. (1978). Note on the uniqueness of the maximum likelihood estimator for the tobit model. Econometrica, 46, 1211-1215.
32. Orme, C. D., & Ruud, P. A. (2002). On the uniqueness of the maximum likelihood estimator. Economics Letters, 75, 209-217.
33. Paninski, L. (2006a). The most likely voltage path and large deviations approximations for integrate-and-fire neurons. Journal of Computational Neuroscience, 21, 71-87.
34. Paninski, L. (2006b). The spike-triggered average of the integrate-and-fire cell driven by Gaussian white noise. Neural Computation, 18, 2592-2616.
35. Paninski, L., Pillow, J., & Lewi, J. (2007). Statistical models for neural encoding, decoding, and optimal stimulus design. In P. Cisek, T. Drew, & J. Kalaska (Eds.), Computational neuroscience: Progress in brain research. Elsevier.
36. Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama Rad, K., Vidne, M., et al. (2010). A new look at state-space models for neural data. Journal of Computational Neuroscience, 29(1), 107-126.
37. Peña, J.-L., & Konishi, M. (2000). Cellular mechanisms for resolving phase ambiguity in the owl's inferior colliculus. Proceedings of the National Academy of Sciences of the United States of America, 97, 11787-11792.
38. Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E., & Chichilnisky, E. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. Journal of Neuroscience, 25, 11003-11013. (Pubitemid 41692630)
39. Pitt, M., & Shephard, N. (1999). Filtering via simulation: Auxiliary particle filters. Journal of the American Statistical Association, 94(446), 590-599.
40. Pospischil, M., Piwkowska, Z., Rudolph, M., Bal, T., & Destexhe, A. (2007). Calculating event-triggered average synaptic conductances from the membrane potential. Journal of Neurophysiology, 97, 2544-2552. (Pubitemid 46411183)
41. Press, W., Teukolsky, S., Vetterling, W., & Flannery, B. (1992). Numerical recipes in C. Cambridge University Press.
42. Priebe, N., & Ferster, D. (2005). Direction selectivity of excitation and inhibition in simple cells of the cat primary visual cortex. Neuron, 45, 133-145. (Pubitemid 40075355)
43. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77, 257-286.
44. Richardson, M. J. E., & Gerstner, W. (2005). Synaptic shot noise and conductance fluctuations affect the membrane voltage with equal significance. Neural Computation, 17(4), 923-947. (Pubitemid 40517883)
45. Sawtell, N. (2010). Multimodal integration in granule cells as a basis for associative plasticity and sensory prediction in a cerebellum-like circuit. Neuron, 66(4), 573-584.
46. Simoncelli, E., Paninski, L., Pillow, J., & Schwartz, O. (2004). Characterization of neural responses with stochastic stimuli. In The cognitive neurosciences (3rd ed.). MIT Press.
47. Vogelstein, J., Watson, B., Packer, A., Jedynak, B., Yuste, R., & Paninski, L. (2009). Model-based optimal inference of spike times and calcium dynamics given noisy and intermittent calcium-fluorescence imaging. Biophysical Journal, 97, 636-655.
48. Vogelstein, J., Packer, A., Machado, T., Sippy, T., Babadi, B., Yuste, R., et al. (2010). Fast nonnegative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 104(6), 3691.
49. Wang, X., Wei, Y., Vaingankar, V., Wang, Q., Koepsell, K., Sommer, F., et al. (2007). Feedforward excitation and inhibition evoke dual modes of firing in the cat's visual thalamus during naturalistic viewing. Neuron, 55, 465-478. (Pubitemid 47126264)
50. Wehr, M., & Zador, A. (2003). Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature, 426, 442-446. (Pubitemid 37490130)
51. Xie, R., Gittelman, J., Pollak, G. (2007). Rethinking tuning: In vivo whole-cell recordings of the inferior colliculus in awake bats. The Journal of Neuroscience, 27(35), 9469.