Adaptive Bayesian Methods for Closed-Loop Neurophysiology

Closed Loop Neuroscience

Volumn , Issue , 2016, Pages 3-18

  Pillow, J W ^a   Park, M ^b  

^a PRINCETON UNIVERSITY   (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

Author keywords

Bayesian active learning; Closed loop experiments; Gaussian processes; Maximum mutual information; Minimum mean square error; Predictive distribution; Receptive field; Tuning curve

Indexed keywords

EID: 85013859890     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1016/B978-0-12-802452-2.00001-9     Document Type: Chapter

References (64)

1. Aguera y., Arcas B., Fairhall A.L. What causes a neuron to spike?. Neural Comput. 2003, 15(8):1789-1807.
2. Benda J., Gollisch T., Machens C.K., Herz A.V. From response to stimulus: adaptive sampling in sensory physiology. Curr. Opin. Neurobiol. 2007, 17(4):430-436.
3. Bernardo J.M. Expected information as expected utility. Ann. Stat. 1979, 7(3):686-690.
4. Bialek W., Rieke F., de Ruyter van Steveninck R.R., Warland D. Reading a neural code. Science 1991, 252:1854-1857.
5. Bölinger D., Gollisch T. Closed-loop measurements of iso-response stimuli reveal dynamic nonlinear stimulus integration in the retina. Neuron 2012, 73(2):333-346.
6. Chaloner K., Verdinelli I. Bayesian experimental design: a review. Stat. Sci. 1995, 10(3):273-304.
7. Chichilnisky E.J. A simple white noise analysis of neuronal light responses. Netw. Comput. Neural Syst. 2001, 12:199-213.
8. Cohn D.A., Ghahramani Z., Jordan M.I. Active learning with statistical models. J. Artif. Intell. Res. 1996, 4:129-145.
9. Cronin B., Stevenson I.H., Sur M., Körding K.P. Hierarchical Bayesian modeling and Markov chain Monte Carlo sampling for tuning-curve analysis. J. Neurophysiol. 2010, 103(1):591-602. http://jn.physiology.org/content/103/1/591.short, 10.1152/jn.00379.2009.
10. Cunningham J.P., Yu B.M., Shenoy K.V., Sahani M. Inferring neural firing rates from spike trains using Gaussian processes. Advances in Neural Information Processing Systems 20 2008, 329-336.
11. David S.V., Gallant J.L. Predicting neuronal responses during natural vision. Netw. Comput. Neural Syst. 2005, 16(2):239-260.
12. deCharms R.C., Blake D.T., Merzenich M.M. Optimizing sound features for cortical neurons. Science 1998, 280(5368):1439-1443.
13. DiMattina C., Zhang K. Active data collection for efficient estimation and comparison of nonlinear neural models. Neural Comput. 2011, 23(9):2242-2288.
14. DiMattina C., Zhang K. Adaptive stimulus optimization for sensory systems neuroscience. Front. Neural Circuits 2013, 7:101.
15. Földiák P. Stimulus optimisation in primary visual cortex. Neurocomputing 2001, 38:1217-1222.
16. Gilks W.R., Berzuini C. Following a moving target-Monte Carlo inference for dynamic Bayesian models. J. R. Stat. Soc. Ser. B 2001, 63(1):127-146.
17. Gollisch T., Herz A.V. The iso-response method: measuring neuronal stimulus integration with closed-loop experiments. Front. Neural Circuits 2012, 6:104.
18. Goris R.L.T., Movshon J.A., Simoncelli E.P. Partitioning neuronal variability. Nat. Neurosci. 2014, 17(6):858-865. http://dx.doi.org/10.1038/nn.3711.
19. Horwitz G.D., Hass C.A. Nonlinear analysis of macaque v1 color tuning reveals cardinal directions for cortical color processing. Nat. Neurosci. 2012, 06, 15(6):913-919.
20. Kapoor A., Horvitz E., Basu S. Selective supervision: guiding supervised learning with decision-theoretic active learning. International Joint Conference on Artificial Intelligence 2007.
21. Kass R., Raftery A. Bayes factors. J. Am. Stat. Assoc. 1995, 90:773-795.
22. Kelly R.C., Smith M.A., Kass R.E., Lee T.S. Local field potentials indicate network state and account for neuronal response variability. J. Comput. Neurosci. 2010, 29(3):567-579.
23. Kim W., Pitt M.A., Lu Z.L., Steyvers M., Myung J.I. A hierarchical adaptive approach to optimal experimental design. Neural Comput. 2014, 2015/02/09, 26(11):2465-2492. http://dx.doi.org/10.1162/NECO_a_00654.
24. King-Smith P.E., Grigsby S.S., Vingrys A.J., Benes S.C., Supowit A. Efficient and unbiased modifications of the quest threshold method: theory, simulations, experimental evaluation and practical implementation. Vis. Res. 1994, 34(7):885-912.
25. Koepsell K., Sommer F.T. Information transmission in oscillatory neural activity. Biol. Cybernet. 2008, 99(4):403-416.
26. Kontsevich L.L., Tyler C.W. Bayesian adaptive estimation of psychometric slope and threshold. Vision Res. 1999, 39:2729-12737.
27. Krause A., Singh A., Guestrin C. Near-optimal sensor placements in Gaussian processes: theory, efficient algorithms and empirical studies. J. Mach. Learn. Res. 2008, 9:235-284. http://portal.acm.org/citation.cfm?id=1390689, 1532-4435.
28. Kuck H., de Freitas N., Doucet A. SMC samplers for Bayesian optimal nonlinear design. Proceedings of the IEEE Nonlinear Statistical Signal Processing Workshop 2006, 99-102. IEEE.
29. Lewi J., Butera R., Paninski L. Sequential optimal design of neurophysiology experiments. Neural Comput. 2009, 21(3):619-687.
30. Lewi J., Butera R., Schneider D.M., Woolley S., Paninski L. Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. Advances in Neural Information Processing Systems 21 2009, 945-952. Curran Associates, Inc., Red Hook, NY. D. Koller, D. Schuurmans, Y. Bengio, L. Bottou (Eds.).
31. Lewi J., Schneider D.M., Woolley S.M.N., Paninski L. Automating the design of informative sequences of sensory stimuli. J. Comput. Neurosci. 2011, Feb, 30(1):181-200. http://dx.doi.org/10.1007/s10827-010-0248-1.
32. Lewis D.D., Gale W.A. A sequential algorithm for training text classifiers. Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval 1994, 3-12. Springer-Verlag.
33. Lindley D. On a measure of the information provided an experiment. Ann. Math. Stat. 1956, 27:986-1005.
34. Luttrell S.P. The use of transinformation in the design of data sampling scheme for inverse problems. Inverse Prob. 1985, 1:199-218.
35. Machens C.K., Gollisch T., Kolesnikova O., Herz A.V.M. Testing the efficiency of sensory coding with optimal stimulus ensembles. Neuron 2005, 47(3):447-456. http://dx.doi.org/10.1016/j.neuron.2005.06.015.
36. Mackay D. Information-based objective functions for active data selection. Neural Comput. 1992, 4:589-603.
37. Macke J.H., Gerwinn S., White L.E., Kaschube M., Bethge M. Gaussian process methods for estimating cortical maps. Neuroimage 2011, 56(2):570-581. http://dx.doi.org/10.1016/j.neuroimage.2010.04.272.
38. Müller P., Parmigiani G. Bayesian Analysis in Statistics and Econometrics: Essays in Honor of Arnold Zellner 1996, 397-406. Wiley, New York.
39. Nelder J.A., Wedderburn R.W. Generalized linear models. J. R. Stat. Soc. Ser. A 1972, 135(3):370-384.
40. Nelken I., Prut Y., Vaadia E., Abeles M. In search of the best stimulus: an optimization procedure for finding efficient stimuli in the cat auditory cortex. Hear. Res. 1994, 72:237-253.
41. O'Connor K.N., Petkov C.I., Sutter M.L. Adaptive stimulus optimization for auditory cortical neurons. J. Neurophysiol. 2005, 94(6):4051-4067.
42. Paninski L. Design of experiments via information theory. Adv. Neural Inform. Process. Syst. 2003, 16:1319-1326.
43. Paninski L. Maximum likelihood estimation of cascade point-process neural encoding models. Netw. Comput. Neural Syst. 2004, 15:243-262.
44. Paninski L. Asymptotic theory of information-theoretic experimental design. Neural Comput. 2005, 17(7):1480-1507.
45. Park M., Horwitz G., Pillow J.W. Active learning of neural response functions with Gaussian processes. Advances in Neural Information Processing Systems 24 2011, 2043-2051. J. Shawe-Taylor, R. Zemel, P. Bartlett, F. Pereira, K. Weinberger (Eds.).
46. Park M., Pillow J.W. Receptive field inference with localized priors. PLoS Comput. Biol. 2011, 7(10):e1002219. http://dx.doi.org/10.1371%2Fjournal.pcbi.1002219.
47. Park M., Pillow J.W. Bayesian active learning with localized priors for fast receptive field characterization. Advances in Neural Information Processing Systems 25 2012, 2357-2365. http://books.nips.cc/papers/files/nips25/NIPS2012_1144.pdf, P. Bartlett, F. Pereira, C. Burges, L. Bottou, K. Weinberger (Eds.).
48. Park M., Pillow J.W. Bayesian inference for low rank spatiotemporal neural receptive fields. Adv. Neural Inform. Process. Syst. 26 2013, 2688-2696. http://media.nips.cc/nipsbooks/nipspapers/paper_files/nips26/1259.pdf.
49. Park M., Weller J.P., Horwitz G.D., Pillow J.W. Adaptive estimation of firing rate maps under super-Poisson variability. Computational and Systems Neuroscience (CoSyNe) Annual Meeting 2013.
50. Park M., Weller J.P., Horwitz G.D., Pillow J.W. Bayesian active learning of neural firing rate maps with transformed Gaussian process priors. Neural Comput. 2014, 26(8):1519-1541. http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00615.
51. Pillow J.W., Ahmadian Y., Paninski L. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Comput. 2011, Jan, 23(1):1-45. http://dx.doi.org/10.1162/NECO_a_00058.
52. Pillow J.W., Shlens J., Paninski L., Sher A., Litke A.M., Chichilnisky E.P., Simoncelli E.J. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 2008, 454:995-999.
53. Rad K.R., Paninski L. Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods. Netw. Comput. Neural Syst. 2010, 21(3-4):142-168.
54. Rasmussen C., Williams C. Gaussian Processes for Machine Learning 2006, MIT Press, Cambridge, MA.
55. Rieke F., Warland D., de Ruyter van Steveninck R., Bialek W. Spikes: Exploring the Neural Code 1996, MIT Press, Cambridge, MA.
56. Roy N., Mccallum A. Toward optimal active learning through sampling estimation of error reduction. Proceedings of the 18th International Conference on Machine Learning 2001, 441-448. Morgan Kaufmann, San Francisco, CA.
57. Rust N.C., Schwartz O., Movshon J.A., Simoncelli E.P. Spatiotemporal elements of macaque V1 receptive fields. Neuron 2005, 46(6):945-956. http://dx.doi.org/10.1016/j.neuron.2005.05.021.
58. Schwartz O., Pillow J.W., Rust N.C., Simoncelli E.P. Spike-triggered neural characterization. J. Vis 2006, 6(4):484-507. http://journalofvision.org/6/4/13/, 1534-7362.
59. Seeger M., Gerwinn S., Bethge M. Bayesian inference for sparse generalized linear models. Machine Learning: ECML 2007, Springer, New York.
60. Truccolo W., Eden U.T., Fellows M.R., Donoghue J.P., Brown E.N. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. J. Neurophysiol. 2005, 93(2):1074-1089.
61. Victor J.D., Nirenberg S. Indices for testing neural codes. Neural Comput. 2008, 20(12):2895-2936. http://dx.doi.org/10.1162/neco.2008.10-07-633.
62. Watson A., Pelli D. QUEST: a Bayesian adaptive psychophysical method. Percept. Psychophys. 1983, 33:113-120.
63. Wu A., Park M., Koyejo O.O., Pillow J.W. Sparse Bayesian structure learning with dependent relevance determination priors. Advances in Neural Information Processing Systems 27 2014, 1628-1636. Curran Associates, Inc. http://papers.nips.cc/paper/5233-sparse-bayesian-structure-learning-with-dependent-relevance-determination-priors.pdf, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, K. Weinberger (Eds.).
64. Yu B.M., Cunningham J.P., Santhanam G., Ryu S.I., Shenoy K.V., Sahani M. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 2009, 102(1):614.