Inferring Nonlinear Neuronal Computation Based on Physiologically Plausible Inputs

PLoS Computational Biology

Volumn 9, Issue 7, 2013, Pages

  McFarland, James M ^a   Cui, Yuwei ^a   Butts, Daniel A ^a  

^a University of Maryland   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PARAMETER ESTIMATION; PHYSIOLOGICAL MODELS;

INPUT MODELS; LINEAR APPROXIMATIONS; LINEAR RECEPTIVE FIELDS; NEURON RESPONSE; NONLINEAR CASCADES; NONLINEAR INPUTS; PHYSIOLOGICAL MECHANISMS; PHYSIOLOGICAL PROCESS; SENSORY NEURONS; SENSORY PROCESSING;

NEURONS;

ARTICLE; AUDITORY NERVOUS SYSTEM; LINEAR SYSTEM; MATHEMATICAL COMPUTING; NERVE CELL STIMULATION; NEUROANATOMY; NEUROPHYSIOLOGY; NONLINEAR SYSTEM; PREDICTION; PROBABILITY; SENSORY NERVE CELL; STATISTICAL MODEL; STATISTICAL PARAMETERS; VISUAL NERVOUS SYSTEM;

EID: 84880838561     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1003143     Document Type: Article

References (101)

1. Riesenhuber M, Poggio T, (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2: 1019-1025.
2. Brincat SL, Connor CE, (2004) Underlying principles of visual shape selectivity in posterior inferotemporal cortex. Nat Neurosci 7: 880-886.
3. Mineault PJ, Khawaja FA, Butts DA, Pack CC, (2012) Hierarchical processing of complex motion along the primate dorsal visual pathway. Proc Natl Acad Sci U S A 109: E972-E980.
4. DiCarlo JJ, Cox DD, (2007) Untangling invariant object recognition. Trends Cogn Sci 11: 333-341.
5. Rust NC, Stocker AA, (2010) Ambiguity and invariance: two fundamental challenges for visual processing. Curr Opin Neurobiol 20: 382-388.
6. Brillinger DR, (1988) Maximum likelihood analysis of spike trains of interacting nerve cells. Biol Cybern 59: 189-200.
7. Paninski L, (2004) Maximum likelihood estimation of cascade point-process neural encoding models. Network 15: 243-262.
8. Truccolo W, Eden UT, Fellows MR, Donoghue JP, Brown EN, (2005) A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J Neurophysiol 93: 1074-1089.
9. Theunissen FE, David SV, Singh NC, Hsu A, Vinje WE, et al. (2001) Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli. Network 12: 289-316.
10. Carandini M, Demb JB, Mante V, Tolhurst DJ, Dan Y, et al. (2005) Do we know what the early visual system does? J Neurosci 25: 10577-10597.
11. Shapley R, (2009) Linear and nonlinear systems analysis of the visual system: why does it seem so linear? A review dedicated to the memory of Henk Spekreijse. Vision Res 49: 907-921.
12. de Ruyter van Steveninck R, Bialek W, (1988) Coding and information transfer in short spike sequences. P Roy Soc Lond B Bio 234: 379-414.
13. Schwartz O, Pillow JW, Rust NC, Simoncelli EP, (2006) Spike-triggered neural characterization. J Vis 6: 484-507.
14. Sharpee T, Rust NC, Bialek W, (2004) Analyzing neural responses to natural signals: maximally informative dimensions. Neural Comput 16: 223-250.
15. Pillow JW, Simoncelli EP, (2006) Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. J Vis 6: 414-428.
16. Chichilnisky EJ, (2001) A simple white noise analysis of neuronal light responses. Network 12: 199-213.
17. Ahrens MB, Paninski L, Sahani M, (2008) Inferring input nonlinearities in neural encoding models. Network 19: 35-67.
18. Park M, Horwitz G, Pillow JW, (2011) Active learning of neural response functions with Gaussian processes. Adv in Neural Inf Process Syst (NIPS) 24: 2043-2051.
19. Truccolo W, Donoghue JP, (2007) Nonparametric modeling of neural point processes via stochastic gradient boosting regression. Neural Comput 19: 672-705.
20. Marmarelis PZ, Marmarelis VZ (1978) Analysis of physiological systems: The white-noise approach. New York: Plenum Press.
21. Schetzen M (1989) The Volterra and Wiener theories of nonlinear systems. Malabar, Florida: Krieger.
22. Eggermont JJ, (1993) Wiener and Volterra analyses applied to the auditory system. Hear Res 66: 177-201.
23. DeWeese MR (1995) Optimization principles for the neural code: Princeton.
24. Marmarelis VZ (2004) Nonlinear Dynamic Modeling of Physiological Systems. Hoboken, NJ: Wiley Interscience.
25. Ahrens MB, Linden JF, Sahani M, (2008) Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods. J Neurosci 28: 1929-1942.
26. Park IM, Pillow JW, (2011) Bayesian spike-triggered covariance analysis. Adv Neural Inf Process Syst (NIPS) 24: 1692-1700.
27. Fitzgerald JD, Rowekamp RJ, Sincich LC, Sharpee TO, (2011) Second order dimensionality reduction using minimum and maximum mutual information models. PLoS Comput Biol 7: e1002249.
28. Rajan K, Bialek W (2012) Maximally informative "stimulus energies" in the analysis of neural responses to natural signals. arXiv:12010321 [q-bioNC].
29. Lau B, Stanley GB, Dan Y, (2002) Computational subunits of visual cortical neurons revealed by artificial neural networks. Proc Natl Acad Sci U S A 99: 8974-8979.
30. Prenger R, Wu MC, David SV, Gallant JL, (2004) Nonlinear V1 responses to natural scenes revealed by neural network analysis. Neural Netw 17: 663-679.
31. Nishimoto S, Gallant JL, Nishimoto S, Gallant JL, (2011) A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. J Neurosci 31: 14551-14564.
32. Mineault PJ, Khawaja FA, Butts DA, Pack CC, (2012) Hierarchical processing of complex motion along the primate dorsal visual pathway. Proc Natl Acad Sci U S A.
33. Berry MJ, Meister M, (1998) Refractoriness and neural precision. J Neurosci 18: 2200-2211.
34. Keat J, Reinagel P, Reid RC, Meister M, (2001) Predicting every spike: a model for the responses of visual neurons. Neuron 30: 803-817.
35. Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ, (2005) Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. J Neurosci 25: 11003-11013.
36. Shapley RM, Victor JD, (1978) The effect of contrast on the transfer properties of cat retinal ganglion cells. J Physiol 285: 275-298.
37. Meister M, Berry MJ, (1999) The neural code of the retina. Neuron 22: 435-450.
38. Mante V, Bonin V, Carandini M, (2008) Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. Neuron 58: 625-638.
39. Wehr M, Zador AM, (2003) Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex. Nature 426: 442-446.
40. Murphy BK, Miller KD, (2009) Balanced amplification: a new mechanism of selective amplification of neural activity patterns. Neuron 61: 635-648.
41. Vogels TP, Abbott LF, (2009) Gating multiple signals through detailed balance of excitation and inhibition in spiking networks. Nat Neurosci 12: 483-491.
42. Sun YJ, Wu GK, Liu BH, Li P, Zhou M, et al. (2010) Fine-tuning of pre-balanced excitation and inhibition during auditory cortical development. Nature 465: 927-931.
43. Dorrn AL, Yuan K, Barker AJ, Schreiner CE, Froemke RC, (2010) Developmental sensory experience balances cortical excitation and inhibition. Nature 465: 932-936.
44. Pillow JW, Shlens J, Paninski L, Sher A, Litke AM, et al. (2008) Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature 454: 995-999.
45. Butts DA, Weng C, Jin J, Alonso JM, Paninski L, (2011) Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression. J Neurosci 31: 11313-11327.
46. Carcieri SM, Jacobs AL, Nirenberg S, (2003) Classification of retinal ganglion cells: a statistical approach. J Neurophysiol 90: 1704-1713.
47. Farrow K, Masland RH, (2011) Physiological clustering of visual channels in the mouse retina. J Neurophysiol 105: 1516-1530.
48. Zhang Y, Kim IJ, Sanes JR, Meister M, (2012) The most numerous ganglion cell type of the mouse retina is a selective feature detector. Proc Natl Acad Sci U S A 109: E2391-2398.
49. Cantrell DR, Cang J, Troy JB, Liu X, (2010) Non-centered spike-triggered covariance analysis reveals neurotrophin-3 as a developmental regulator of receptive field properties of ON-OFF retinal ganglion cells. PLoS Comput Biol 6: e1000967.
50. Demb JB, Zaghloul K, Haarsma L, Sterling P, (2001) Bipolar cells contribute to nonlinear spatial summation in the brisk-transient (Y) ganglion cell in mammalian retina. J Neurosci 21: 7447-7454.
51. Kim KJ, Rieke F, (2001) Temporal contrast adaptation in the input and output signals of salamander retinal ganglion cells. J Neurosci 21: 287-299.
52. Geffen MN, de Vries SEJ, Meister M, (2007) Retinal ganglion cells can rapidly change polarity from Off to On. PLoS Biol 5: e65.
53. Gollisch T, Meister M, (2008) Modeling convergent ON and OFF pathways in the early visual system. Biol Cybern 99: 263-278.
54. Schwartz GW, Okawa H, Dunn FA, Morgan JL, Kerschensteiner D, et al. (2012) The spatial structure of a nonlinear receptive field. Nat Neurosci 15: 1572-1580.
55. Fairhall AL, Burlingame CA, Narasimhan R, Harris RA, Puchalla JL, et al. (2006) Selectivity for multiple stimulus features in retinal ganglion cells. J Neurophysiol 96: 2724-2738.
56. Rad KR, Paninski L, (2010) Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods. Network 21: 142-168.
57. Rust NC, Schwartz O, Movshon JA, Simoncelli EP, (2005) Spatiotemporal elements of macaque v1 receptive fields. Neuron 46: 945-956.
58. Gerwinn S, Macke JH, Seeger M, Bethge M, (2008) Bayesian inference for spiking neuron models with a sparsity prior. Adv in Neural Inf Proces Syst (NIPS) 20: 529-536.
59. Narendra K, Gallman P, (1966) An iterative method for the identification of nonlinear systems using a Hammerstein model. IEEE T Automat Contr 11: 546-550.
60. Hunter IW, Korenberg MJ, (1986) The identification of nonlinear biological systems: Wiener and Hammerstein cascade models. Biol Cybern 55: 135-144.
61. Schinkel-Bielefeld N, David SV, Shamma SA, Butts DA, (2012) Inferring the role of inhibition in auditory processing of complex natural stimuli. J Neurophysiol 107: 3296-3307.
62. Lochmann T, Blanche TJ, Butts DA, (2013) Construction of direction selectivity through local energy computations in primary visual cortex. PLoS ONE 8: e58666.
63. Friedman JH, Stuetzle W, (1981) Projection pursuit regression. J Am Stat Assoc 76: 817-823.
64. Lingjærde OC, Liestøl K, (1998) Generalized projection pursuit regression. SIAM J Sci Comput 20: 844-857.
65. Vintch B, Zaharia A, Movshon JA, Simoncelli EP, (2012) Efficient and direct estimation of a neural subunit model for sensory coding. Adv in Neural Information Processing Systems (NIPS) 25: 3113-3121.
66. Bertsekas DP (1999) Nonlinear Programming. Belmont, MA: Athena Scientific.
67. Paninski L, (2003) Convergence properties of three spike-triggered analysis techniques. Network 14: 437-464.
68. J KM, M SH, K N, (1989) Dissection of the neuron network in the catfish inner retina. III. Interpretation of spike kernels. J Neurophysiol 61: 1110-1120.
69. Field DJ, (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4: 2379-2394.
70. Olshausen BA, Field DJ, (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381: 607-609.
71. Lewicki MS, (2002) Efficient coding of natural sounds. Nat Neurosci 5: 356-363.
72. Gabernet L, Jadhav SP, Feldman DE, Carandini M, Scanziani M, (2005) Somatosensory integration controlled by dynamic thalamocortical feed-forward inhibition. Neuron 48: 315-327.
73. Okun M, Lampl I, (2008) Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities. Nat Neurosci 11: 535-537.
74. Wu GK, Arbuckle R, Liu BH, Tao HW, Zhang LI, (2008) Lateral sharpening of cortical frequency tuning by approximately balanced inhibition. Neuron 58: 132-143.
75. Lesica NA, Weng C, Jin J, Yeh CI, Alonso JM, et al. (2006) Dynamic encoding of natural luminance sequences by LGN bursts. PLoS Biol 4: e209.
76. Butts DA, Desbordes G, Weng C, Jin J, Alonso JM, et al. (2010) The episodic nature of spike trains in the early visual pathway. J Neurophysiol 104: 3371-3387.
77. Butts DA, Weng C, Jin J, Yeh CI, Lesica NA, et al. (2007) Temporal precision in the neural code and the timescales of natural vision. Nature 449: 92-95.
78. Sincich LC, Horton JC, Sharpee TO, (2009) Preserving information in neural transmission. J Neurosci 29: 6207-6216.
79. Wang X, Vaingankar V, Sanchez CS, Sommer FT, Hirsch JA, (2011) Thalamic interneurons and relay cells use complementary synaptic mechanisms for visual processing. Nat Neurosci 14: 224-231.
80. Kaplan E, Marcus S, So Y, (1979) Effects of dark adaptation on spatial and temporal properties of receptive fields in cat lateral geniculate nucleus. J Physiol 294: 561-580.
81. Hsu A, Woolley SM, Fremouw TE, Theunissen FE, (2004) Modulation power and phase spectrum of natural sounds enhance neural encoding performed by single auditory neurons. J Neurosci 24: 9201-9211.
82. Gill P, Zhang J, Wooley SMN, Fremouw T, Theunissen FE, (2006) Sound representation methods for spectro-temporal receptive field estimation. J Comput Neurosci 21: 5-20.
83. Teeters JL, Harris KD, Millman KJ, Olshausen BA, Sommer FT, (2008) Data sharing for computational neuroscience. Neuroinformatics 6: 47-55.
84. Calabrese A, Schumacher JW, Schneider DM, Paninski L, Woolley SMN, (2011) A Generalized Linear Model for Estimating Spectrotemporal Receptive Fields from Responses to Natural Sounds. PLoS ONE 6: e16104.
85. Adelson EH, Bergen JR, (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am A 2: 284-299.
86. Emerson RC, Bergen JR, Adelson EH, (1992) Directionally selective complex cells and the computation of motion energy in cat visual cortex. Vision Res 32: 203-218.
87. Mante V, Carandini M, (2005) Mapping of stimulus energy in primary visual cortex. J Neurophysiol 94: 788-798.
88. Touryan J, Lau B, Dan Y, (2002) Isolation of relevant visual features from random stimuli for cortical complex cells. J Neurosci 22: 10811-10818.
89. Tanabe S, Haefner RM, Cumming BG, (2011) Suppressive mechanisms in monkey V1 help to solve the stereo correspondence problem. J Neurosci 31: 8295-8305.
90. Diaconis P, Shahshahani M, (1984) On nonlinear functions of linear combinations. SIAM J Sci and Stat Comput 5: 175-191.
91. Blanche TJ, Spacek MA, Hetke JF, Swindale NV, (2005) Polytrodes: high-density silicon electrode arrays for large-scale multiunit recording. J Neurophysiol 93: 2987-3000.
92. Nocedal J, (1980) Updating quasi-Newton matrices with limited storage. Math Comput 35: 773-782.
93. Saleem AB, Krapp HG, Shultz SR, (2008) Receptive field characterization by spike-triggered independent component analysis. J Vis 8: 2.1-2.16.
94. Sahani M, Linden J (2003) Evidence optimization techniques for estimating stimulus-response functions. In: Becker S, Thrun S, Obermayer K, editors. Adv in Neural Information Processing Systems. Cambridge, MA: The MIT Press. pp. 317-324.
95. David SV, Mesgarani N, Shamma SA, (2007) Estimating sparse spectro-temporal receptive fields with natural stimuli. Network 18: 191-212.
96. Gerwinn S, Macke JH, Bethge M, (2010) Bayesian inference for generalized linear models for spiking neurons. Front Comput Neurosci 4: 12.
97. Park M, Pillow JW, (2011) Receptive field inference with localized priors. PLoS Comput Biol 7: e1002219.
98. Kouh M, Sharpee TO, (2009) Estimating linear-nonlinear models using Renyi divergences. Network 20: 49-68.
99. Sahani M, Linden J (2003) How linear are auditory cortical responses? In: Becker S, Thrun S, Obermayer K, editors. Advances in neural information processing systems. Cambridge: MIT.
100. Kayser C, Salazar RF, Konig P, (2003) Responses to natural scenes in cat V1. J Neurophysiol 90: 1910-1920.
101. Cai D, Deangelis GC, Freeman RD, (1997) Spatiotemporal receptive field organization in the lateral geniculate nucleus of cats and kittens. J Neurophysiol 78: 1045-1061.