Spike-triggered neural characterization

Journal of Vision

Volumn 6, Issue 4, 2006, Pages 484-507

  Schwartz, Odelia ^a   Pillow, Jonathan W ^b   Rust, Nicole C ^c   Simoncelli, Eero P ^d  

^a HOWARD HUGHES MEDICAL INSTITUTE   (United States)

^b UNIVERSITY COLLEGE LONDON   (United Kingdom)

^c MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^d NEW YORK UNIVERSITY   (United States)

Author keywords

Characterization; Neural response; Nonlinear; Receptive field; Reverse correlation; Spike triggered analysis

Indexed keywords

ANALYSIS OF COVARIANCE; ARTICLE; DATA ANALYSIS; EXPERIMENTAL STUDY; FILTER; LINEAR SYSTEM; MATHEMATICAL MODEL; METHODOLOGY; NERVE CELL; NONLINEAR SYSTEM; RECEPTIVE FIELD; SENSORY NERVE CELL; SIMULATION; SPIKE WAVE; STATISTICAL MODEL;

ACTION POTENTIALS; COMPUTER SIMULATION; HUMANS; LINEAR MODELS; MODELS, NEUROLOGICAL; NEURONS, AFFERENT; NONLINEAR DYNAMICS; POISSON DISTRIBUTION;

EID: 33746478189     PISSN: 15347362     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/6.4.13     Document Type: Article

References (82)

1. Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, Optics and Image Science, 2, 284-299. [PubMed]
2. Agüera y Arcas, B., & Fairhall, A. L. (2003). What causes a neuron to spike? Neural Computation, 15, 1789-1807. [PubMed]
3. Agüera y Arcas, B., Fairhall, A. L., & Bialek, W. (2001). What can a single neuron compute? In T. K. Leen, T. G. Dietterich, & V. Tresp (Eds.), Adv. Neural Information Processing Systems (NIPS*00) (Vol. 13, pp. 75-81). Cambridge, MA: MIT Press.
4. Agüera y Arcas, B., Fairhall, A. L., & Bialek, W. (2003). Computation in a single neuron: Hodgkin and Huxley revisited. Neural Computation, 15, 1715-1749. [PubMed]
5. Albrecht, D. G., & Geisler, W. S. (1991). Motion sensitivity and the contrast-response function of simple cells in the visual cortex. Visual Neuroscience, 7, 531-546. [PubMed]
6. Anzai, A., Ohzawa, I., & Freeman, R. D. (1999). Neural mechanisms for processing binocular information: I. Simple cells. Journal of Neurophysiology, 82, 891-908. [PubMed] [Article]
7. Bair, W., Cavanaugh, J. R., & Movshon, J. A. (1997). Reconstructing stimulus velocity from neuronal responses in area MT. In M. C. Mozer, M. I. Jordan, & T. Petsche (Eds.), Adv. Neural Information Processing Systems (NIPS*96) (Vol. 9, pp. 34-40). Cambridge, MA: MIT Press.
8. Berry, M. J. II, & Meister, M. (1998). Refractoriness and neural precision. The Journal of Neuroscience, 18, 2200-2211. [PubMed] [Article]
9. Bialek, W., & de Ruyter van Steveninck, R. (2005). Features and dimensions: Motion estimation in fly vision. http://arxiv.org/abs/q-bio/ 0505003
10. Borghuis, B. G., Perge, J. A., Vajda, I., van Wezel, R. J., van de Grind, W. A., & Lankheet, M. J. (2003). The motion reverse correlation (MRC) method: A linear systems approach in the motion domain. Journal of Neuroscience Methods, 123, 153-166. [PubMed]
11. Brenner, N., Bialek, W., & de Ruyter van Steveninck R. (2000). Adaptive rescaling maximizes information transmission. Neuron, 26, 695-702. [PubMed] [Article]
12. Bussgang, J. J. (1952). Cross-correlation functions of amplitude-distorted gaussian signals. RLE Technical Reports, 216. Cambridge, MA: Research Laboratory of Electronics.
13. Chichilnisky, E. J. (2001). A simple white noise analysis of neuronal light responses. Network, 12, 199-213. [PubMed]
14. Daugman, J. G. (1989). Entropy reduction and decorrelation in visual coding by oriented neural receptive fields. IEEE Transactions on Bio-medical Engineering, 36, 107-114. [PubMed]
15. David, S. V., & Gallant, J. L. (2005). Predicting neuronal responses during natural vision. Network, 16, 239-260. [PubMed]
16. David, S. V., Vinje, W. E., & Gallant, J. L. (2004). Natural stimulus statistics alter the receptive field structure of v1 neurons. The Journal of Neuroscience, 24, 6991-7006. [PubMed] [Article]
17. Dayan, P., & Abbott, L. F. (2001). Theoretical neuroscience: Computational and mathematical modeling of neural systems. Cambridge, MA: MIT Press.
18. DeAngelis, G. C., Ohzawa, I., & Freeman, R. D. (1993). Spatiotemporal organization of simple cell receptive fields in the cat's striate cortex: II. Linearity of temporal and spatial summation. Journal of Neurophysiology, 69, 1118-1135. [PubMed]
19. deBoer, E., & Kuyper, P. (1968). Triggered correlation. IEEE Transactions on Biomedical Engineering, 15, 169-179. [PubMed]
20. de Ruyter van Steveninck, R., & Bialek, W. (1988). Coding and information transfer in short spike sequences. Proceedings of the Royal Society of London B, Biological Sciences, 234, 379-414.
21. Eggermont, J. J., Johannesma, P. M., & Aertsen, A. M. (1983). Reverse-correlation methods in auditory research. Quarterly Reviews of Biophysics, 16, 341-414. [PubMed]
22. Emerson, R. C., Bergen, J. R., & Adelson, E. H. (1992). Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Research, 32, 203-218. [PubMed]
23. Emerson, R. C., Citron, M. C., Vaughn, W. J., & Klein, S. A. (1987). Nonlinear directionally selective sub-units in complex cells of cat striate cortex. Journal of Neurophysiology, 58, 33-65. [PubMed]
24. Fairhall, A. L., Lewen, G. D., Bialek, W., & de Ruyter Van Steveninck, R. R. (2001). Efficiency and ambiguity in an adaptive neural code. Nature, 412, 787-792. [PubMed]
25. Felsen, G., & Dan, Y. (2005). A natural approach to studying vision. Nature Neuroscience, 8, 1643-1646. [PubMed]
26. Field, D. J. (1987). Relations between the statistics of natural images and the response properties of cortical cells. Journal of the Optical Society of America A, Optics and Image Science, 4, 2379-2394. [PubMed]
27. Geisler, D. (1992). Two-tone suppression by a saturating feedback model of the cochlear partition. Hearing Research, 63, 203-211.
28. Harris, K. D., Csicsvari, J., Hirase, H., Dragoi, G., & Buzsáki, G. (2003). Organization of cell assemblies in the hippocampus. Nature, 424, 552-556. [PubMed]
29. Heeger, D. J. (1992). Normalization of cell responses in cat striate cortex. Visual Neuroscience, 9, 181-197. [PubMed]
30. Hochstein, S., & Shapley, R. M. (1976). Linear and nonlinear spatial subunits in Y cat retinal ganglion cells. The Journal of Physiology, 262, 265-284. [PubMed] [Article]
31. Horwitz, G. D., Chichilnisky, E. J., & Albright, T. D. (2005). Blue-yellow signals are enhanced by spatiotemporal luminance contrast in macaque V1. Journal of Neurophysiology, 93, 2263-2278. [PubMed] [Article]
32. Jones, J. P., & Palmer, L. A. (1987). The two-dimensional spatial structure of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58, 1187-1211. [PubMed]
33. Keat, J., Reinagel, P., Reid, R. C., & Meister, M. (2001). Predicting every spike: A model for the responses of visual neurons. Neuron, 30, 803-817. [PubMed] [Article]
34. Korenberg, M. J., Sakai, H. M., & Naka, K. (1989). Dissection of the neuron network in the catfish inner retina: III. Interpretation of spike kernels. Journal of Neurophysiology, 61, 1110-1120. [PubMed]
35. Lau, B., Stanley, G. B., & Dan, Y. (2002). Computational subunits of visual cortical neurons revealed by artificial neural networks. Proceedings of the National Academy of Sciences of the United States of America, 99, 8974-8979. [PubMed] [Article]
36. Lehky, S. R., Sejnowski, T. J., & Desimone, R. (1992). Predicting responses of nonlinear neurons in monkey striate cortex to complex patterns. Journal of Neuroscience, 12, 3568-3581. [PubMed] [Article]
37. Lesica, N. A., & Stanley, G. B. (2004). Encoding of natural scene movies by tonic and burst spikes in the lateral geniculate nucleus. The Journal of Neuroscience, 24, 10731-10740. [PubMed] [Article]
38. Marmarelis, P. Z., & Marmarelis, V. Z. (1978). Analysis of physiological systems: The white noise approach. London: Plenum Press.
39. McLean, J., & Palmer, L. A. (1989). Contribution of linear spatiotemporal receptive field structure to velocity selectivity of simple cells in area 17 of cat. Vision Research, 29, 675-679. [PubMed]
40. Meister, M., Pine, J., & Baylor, D. A. (1994). Multi-neuronal signals from the retina: Acquisition and analysis. Journal of Neuroscience Methods, 51, 95-106. [PubMed]
41. Nishimoto, S., Ishida, T., & Ohzawa, I. (2006). Receptive field properties of neurons in the early visual cortex revealed by local spectral reverse correlation. The Journal of Neuroscience, 26, 3269-3280. [PubMed]
42. Nykamp, D. (2003). White noise analysis of coupled linear-nonlinear systems. SIAM Journal on Applied Mathematics, 63, 1208-1230.
43. Nykamp, D. Q., & Ringach, D. L. (2002). Full identification of a linear-nonlinear system via cross-correlation analysis. Journal of Vision, 2(1), 1-11, http://journalofvision.org/2/1/1/, doi:10.1167/ 2.1.1. [PubMed] [Article]
44. Okatan, M., Wilson, M. A., & Brown, E. N. (2005). Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 17, 1927-1961. [PubMed]
45. Paninski, L. (2003). Convergence properties of some spike-triggered analysis techniques. Network, 14, 437-464. [PubMed]
46. Paninski, L., Fellows, M., Shoham, S., Hatsopoulos, N., & Donoghue, J. (2004). Nonlinear population models for the encoding of dynamic hand position signals in MI. Neurocomputing. Paper presented at Computational Neuroscience, Alicante, Spain, July 2003.
47. Pillow, J. W., Paninski, L., Uzzell, V. J., Simoncelli, E. P., & Chichilnisky, E. J. (2005). Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. The Journal of Neuroscience, 25, 11003-11013. [PubMed] [Article]
48. Pillow, J. W., Shlens, J., Paninski, L., Chichilnisky, E. J., & Simoncelli, E. P. (2005a). Modeling multineuronal responses in primate retinal ganglion cells. In Computational and Systems Neuroscience (CoSyNe), Salt Lake City, UT.
49. Pillow, J. W., Shlens, J., Paninski, L., Chichilnisky, E. J., & Simoncelli, E. P. (2005b). Modeling the correlated spike responses of a cluster of primate retinal ganglion cells. In Annual Meeting, Neurosciences, Washington, DC.
50. Pillow, J. W., & Simoncelli, E. P. (2003). Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing, 52-54, 109-115.
51. Pillow, J. W., & Simoncelli, E. P. (2006). Dimensionality reduction in neural models: An information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6(4), 414-428, http://www.journalofvision.org/6/4/9/, doi:10.1167/6.4.9. [PubMed] [Article]
52. Pillow, J. W., Simoncelli, E. P., & Chichilnisky, E. J. (2003). Characterization of nonlinear spatiotemporal properties of macaque retinal ganglion cells using spike-triggered covariance. In Annual Meeting, Neurosciences, New Orleans.
53. Pillow, J. W., Simoncelli, E. P., & Chichilnisky, E. J. (2004). Characterization of macaque retinal ganglion cell responses using spike-triggered covariance. In Computational and Systems Neuroscience (CoSyNe), Cold Spring Harbor, NY.
54. Prenger, R., Wu, M. C., David, S. V., & Gallant, J. L. (2004). Nonlinear V1 responses to natural scenes revealed by neural network analysis. Neural Networks, 17, 663-679. [PubMed]
55. Reich, D. S., Victor, J. D., & Knight, B. W. (1998). The power ratio and the interval map: Spiking models and extracellular recordings. The Journal of Neuroscience, 18, 10090-10104. [PubMed] [Article]
56. Reid, R. C., & Alonso, J. M. (1995). Specificity of monosynaptic connections from thalamus to visual cortex. Nature, 378, 281-284. [PubMed]
57. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
58. Ringach, D. L. (2004). Mapping receptive fields in primary visual cortex. The Journal of Physiology, 558, 717-728. [PubMed] [Article]
59. Ringach, D. L., Hawken, M. J., & Shapley, R. (2002). Receptive field structure of neurons in monkey primary visual cortex revealed by stimulation with natural image sequences. Journal of Vision, 2(1), 12-24, http://journalofvision.org/2/1/2/, doi:10.1167/2.1.2. [PubMed] [Article]
60. Ringach, D. L., Sapiro, G., & Shapley, R. (1997). A subspace reverse-correlation technique for the study of visual neurons. Vision Research, 37, 2455-2464. [PubMed]
61. Rust, N. C., & Movshon, J. A. (2005). In praise of artifice. Nature Neuroscience, 8, 1647-1650. [PubMed]
62. Rust, N. C., Schwartz, O., Movshon, J. A., & Simoncelli, E. P. (2004). Spike-triggered characterization of excitatory and suppressive stimulus dimensions in monkey V1. Neurocomputing, 58-60C, 793-799.
63. Rust, N. C., Schwartz, O., Movshon, J. A., & Simoncelli, E. P. (2005). Spatiotemporal elements of macaque V1 receptive fields. Neuron, 46, 945-956. [PubMed] [Article]
64. Rust, N. C., Simoncelli, E. P., & Movshon, J. A. (2005). How macaque MT cells compute pattern motion. In Annual Meeting, Neurosciences, Washington, DC.
65. Sakai, H. M., & Naka, K. (1987). Signal transmission in the catfish retina: V. Sensitivity and circuit. Journal of Neurophysiology, 58, 1329-1350. [PubMed]
66. Sakai, K., & Tanaka, S. (2000). Spatial pooling in the second-order spatial structure of cortical complex cells. Vision Research, 40, 855-871. [PubMed]
67. Schwartz, O., Chichilnisky, E. J., & Simoncelli, E. P. (2002). Characterizing neural gain control using spike-triggered covariance. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Adv. Neural Information Processing Systems (NIPS*01) (Vol. 14, pp. 269-276). Cambridge, MA: MIT Press.
68. Schwartz, O., & Simoncelli, E. P. (2001). A spike-triggered covariance method for characterizing divisive normalization models. In First Annual Meeting, Vision Sciences Society, Sarasota.
69. Sen, K., Wright, B. D., Doupe, A. J., & Bialek, W. (2000). Discovering features of natural stimuli relevant to forebrain auditory neurons. In Society for Neuroscience Abstracts (Vol. 26, p. 1475). Washington, DC.
70. Sharpee, T., Rust, N. C., & Bialek, W. (2003). Maximizing informative dimensions: Analyzing neural responses to natural signals. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Adv. Neural Information Processing Systems (NIPS*02) (Vol. 15, pp. 261-268). Cambridge, MA: MIT Press.
71. Sharpee, T., Rust, N. C., & Bialek, W. (2004). Analyzing neural responses to natural signals: Maximally informative dimensions. Neural Computation, 16, 223-250. [PubMed]
72. Sharpee, T. O., Sugihara, H., Kurgansky, A. V., Rebrik, S. P., Stryker, M. P., & Miller, K. D. (2006). Adaptive filtering enhances information transmission in visual cortex. Nature, 439, 936-942. [PubMed]
73. Simoncelli, E. P., Pillow, J., Paninski, L., & Schwartz, O. (2004). Characterization of neural responses with stochastic stimuli. In M. Gazzaniga (Ed.), The cognitive neurosciences (3rd ed., pp 327-338). Cambridge, MA: MIT Press.
74. Smyth, D., Willmore, B., Baker, G. E. Thompson, I. D., & Tolhurst, D. J. (2003). The receptive-field organization of simple cells in primary visual cortex of ferrets under natural scene stimulation. The Journal of Neuroscience, 23, 4746-4759. [PubMed] [Article]
75. Szulborski, R. G., & Palmer, L. A. (1990). The two-dimensional spatial structure of nonlinear subunits in the receptive fields of complex cells. Vision Research, 30, 249-254. [PubMed]
76. Theunissen, F. E., David, S. V., Singh, N. C., Hsu, A., Vinje, W. E., & Gallant, J. L. (2001). Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network, 12, 289-316. [PubMed]
77. Touryan, J., Felsen, G., & Dan, Y. (2005). Spatial structure of complex cell receptive fields measured with natural images. Neuron, 45, 781-791. [PubMed] [Article]
78. Touryan, J., Lau, B., & Dan, Y. (2002). Isolation of relevant visual features from random stimuli for cortical complex cells. The Journal of Neuroscience, 22, 10811-10818. [PubMed] [Article]
79. Truccolo, W., Eden, U. T., Fellows, M. R., Donoghue, J. P., & Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. Journal of Neurophysiology, 93, 1074-1089. [PubMed] [Article]
80. Tsodyks, M., Kenet, T., Grinvald, A., & Arieli, A. (1999). Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science, 286, 1943-1946. [PubMed]
81. Volterra, V. (1959). Theory of functionals and of integro and integro-differential equations. New York: Dover.
82. Wiener, N. (1958). Nonlinear problems in random theory. New York: Wiley.