Dimensionality reduction for large-scale neural recordings

Nature Neuroscience

Volumn 17, Issue 11, 2014, Pages 1500-1509

  Cunningham, John P ^a   Yu, Byron M ^b,c  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; DATA ANALYSIS; DECISION MAKING; DIMENSIONALITY REDUCTION; INTUITION; MOTIVATION; MOTOR PERFORMANCE; MOTOR SYSTEM; NERVE CELL; NERVE POTENTIAL; NEUROLOGIC EXAMINATION; NEUROSCIENCE; POPULATION DYNAMICS; POPULATION RESEARCH; PREFRONTAL CORTEX; REVIEW; SENSORY STIMULATION; STIMULUS; TIME SERIES ANALYSIS; ANIMAL; HUMAN; INFORMATION PROCESSING; NERVE CELL NETWORK; PHYSIOLOGY; STATISTICS;

ANIMALS; DATA COLLECTION; HUMANS; NERVE NET; NEURONS; STATISTICS AS TOPIC;

EID: 84908555723     PISSN: 10976256     EISSN: 15461726     Source Type: Journal    
DOI: 10.1038/nn.3776     Document Type: Review

References (100)

1. Kipke, D.R. et al. Advanced neurotechnologies for chronic neural interfaces: new horizons and clinical opportunities. J. Neurosci. 28, 11830-11838 (2008
2. Kerr, J.N. & Denk, W. Imaging in vivo: watching the brain in action. Nat. Rev. Neurosci. 9, 195-205 (2008
3. Ahrens, M.B. et al. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413-420 (2013
4. Mante, V. et al. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78-84 (2013
5. Kaufman, M.T. et al. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440-448 (2014
6. Mazor, O. & Laurent, G. Transient dynamics versus fixed points in odor representations by locust antennal lobe projection neurons. Neuron 48, 661-673 (2005
7. Harvey, D.C. et al. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62-68 (2012
8. Stokes, M.G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364-375 (2013
9. Briggman, K.L., Abarbanel, H.D.I. & Kristan, W.B. Jr. Optical imaging of neuronal populations during decision-making. Science 307, 896-901 (2005
10. Churchland, M.M. et al. Stimulus onset quenches neural variability: a widespread cortical phenomenon. Nat. Neurosci. 13, 369-378 (2010
11. Churchland, M.M. et al. Neural population dynamics during reaching. Nature 487, 51-56 (2012
12. Yu, B.M. et al. Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. J. Neurophysiol. 102, 614-635 (2009
13. Broome, B.M. et al. Encoding and decoding of overlapping odor sequences. Neuron 51, 467-482 (2006
14. Saha, D. et al. A spatiotemporal coding mechanism for background-invariant odor recognition. Nat. Neurosci. 16, 1830-1839 (2013
15. Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585-590 (2013
16. Machens, C.K. et al. Functional, but not anatomical, separation of 'what' and 'when' in prefrontal cortex. J. Neurosci. 30, 350-360 (2010
17. Cohen, M.R. & Maunsell, J.H.R. A neuronal population measure of attention predicts behavioral performance on individual trials. J. Neurosci. 30, 15241-15253 (2010
18. Luczak, A. et al. Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron 62, 413-425 (2009
19. Durstewitz, D. et al. Abrupt transitions between prefrontal neural ensemble states accompany behavioral transitions during rule learning. Neuron 66, 438-448 (2010
20. Bouchard, K.E. et al. Functional organization of human sensorimotor cortex for speech articulation. Nature 495, 327-332 (2013
21. Brown, E.N. et al. Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456-461 (2004
22. Seidemann, E., Meilijson, I., Abeles, M., Bergman, H. & Vaadia, E. Simultaneously recorded single units in the frontal cortex go through sequences of discrete and stable states in monkeys performing a delayed localization task. J. Neurosci. 16, 752-768 (1996
23. Jones, L.M. et al. Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles. Proc. Natl. Acad. Sci. USA 104, 18772-18777 (2007
24. Ponce-Alvarez, A. et al. Dynamics of cortical neuronal ensembles transit from decision making to storage for later report. J. Neurosci. 32, 11956-11969 (2012
25. Horwitz, G.D. & Newsome, W.T. Target selection for saccadic eye movements: prelude activity in the superior colliculus during a direction-discrimination task. J. Neurophysiol. 86, 2543-2558 (2001
26. Bollimunta, A., Totten, D. & Ditterich, J. Neural dynamics of choice: single-trial analysis of decision-related activity in parietal cortex. J. Neurosci. 32, 12684-12701 (2012
27. Sanger, T.D. & Kalaska, J.F. Crouching tiger, hidden dimensions. Nat. Neurosci. 17, 338-340 (2014
28. Laurent, G. Olfactory network dynamics and the coding of multidimensional signals. Nat. Rev. Neurosci. 3, 884-895 (2002
29. Churchland, M.M et al. Cortical preparatory activity: representation of movement or first cog in a dynamical machine?. Neuron 68, 387-400 (2010
30. Vogels, T.P. et al. Neural network dynamics. Annu. Rev. Neurosci. 28, 357-376 (2005
31. Nawrot, M.P. et al. Measurement of variability dynamics in cortical spike trains. J. Neurosci. Methods 169, 374-390 (2008
32. Churchland, M.M. & Abbott, L.F. Two layers of neural variability. Nat. Neurosci. 15, 1472-1474 (2012
33. Cunningham, J.P. et al. Methods for estimating neural firing rates, and their application to brain-machine interfaces. Neural Netw. 22, 1235-1246 (2009
34. Cox, D.R. & Isham, V. Point Processes (Chapman and Hall, London, 1980
35. Tolhurst, D.J., Movshon, J.A. & Dean, A.F. The statistical reliability of signals in single neurons in cat and monkey visual cortex. Vision Res. 23, 775-785 (1983
36. Afshar, A. et al. Single-trial neural correlates of arm movement preparation. Neuron 71, 555-564 (2011
37. Levi, R. et al. The role of sensory network dynamics in generating a motor program. J. Neurosci. 25, 9807-9815 (2005
38. Sasaki, T., Matsuki, N. & Ikegaya, Y. Metastability of active ca3 networks. J. Neurosci. 27, 517-528 (2007
39. Ecker, A.S. et al. State dependence of noise correlations in macaque primary visual cortex. Neuron 82, 235-248 (2014
40. Jun, J.K. et al. Heterogenous population coding of a short-term memory and decision task. J. Neurosci. 30, 916-929 (2010
41. Shenoy, K.V. et al. Cortical control of arm movements: a dynamical systems perspective. Annu. Rev. Neurosci. 36, 337-359 (2013
42. Ames, K.C. et al. Neural dynamics of reaching following incorrect or absent motor preparation. Neuron 81, 438-451 (2014
43. Sadtler, P.T. et al. Neural constraints on learning. Nature (in the press
44. Stopfer, M., Jayaraman, V. & Laurent, G. Intensity versus identity coding in an olfactory system. Neuron 39, 991-1004 (2003
45. Brown, S.L. et al. Encoding a temporally structured stimulus with a temporally structured neural representation. Nat. Neurosci. 8, 1568-1576 (2005
46. Bathellier, B. et al. Dynamic ensemble odor coding in the mammalian olfactory bulb: sensory information at different timescales. Neuron 57, 586-598 (2008
47. Geffen, M.N. et al. Neural encoding of rapidly fluctuating odors. Neuron 61, 570-586 (2009
48. Raman, B., Joseph, J., Tang, J. & Stopfer, M. Temporally diverse firing patterns in olfactory receptor neurons underlie spatiotemporal neural codes for odors. J. Neurosci. 30, 1994-2006 (2010
49. Sussillo, D. & Abbott, L.F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544-557 (2009
50. Ahrens, M.B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471-477 (2012
51. Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods doi:10.1038/nmeth.3041 (27 July 2014
52. Nicolelis, M.A., Baccala, L.A., Lin, R.C. & Chapin, J.K. Sensorimotor encoding by synchronous neural ensemble activity at multiple levels of the somatosensory system. Science 268, 1353-1358 (1995
53. Paz, R., Natan, C., Boraud, T., Bergman, H. & Vaadia, E. Emerging patterns of neuronal responses in supplementary and primary motor areas during sensorimotor adaptation. J. Neurosci. 25, 10941-10951 (2005
54. Lin, L. et al. Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. Proc. Natl. Acad. Sci. USA 102, 6125-6130 (2005
55. Narayanan, N.S. et al. Delay activity in rodent frontal cortex during a simple reaction time task. J. Neurophysiol. 101, 2859-2871 (2009
56. Bartho, P. et al. Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis. Eur. J. Neurosci. 30, 1767-1778 (2009
57. Brendel, W. et al. Demixed principal component analysis. in Adv. Neural Inf. Process. Syst. 24, 2654-2662 (2011
58. Carrillo-Reid, L. et al. Encoding network states by striatal cell assemblies. J. Neurophysiol. 99, 1435-1450 (2008
59. Hallem, E.A. & Carlson, J.R. Coding of odors by a receptor repertoire. Cell 125, 143-160 (2006
60. Cowley, B.R. et al. Datahigh: graphical user interface for visualizing and interacting with high-dimensional neural activity. J. Neural Eng. 10, 066012 (2013
61. Roweis, S. & Ghahramani, Z. A unifying review of linear gaussian models. Neural Comput. 11, 305-345 (1999
62. Brown, E.N. et al. A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. J. Neurosci. 18, 7411-7425 (1998
63. Eden, U.T. et al. Dynamic analysis of neural encoding by point process adaptive filtering. Neural Comput. 16, 971-998 (2004
64. Truccolo, W. et al. A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. J. Neurophysiol. 93, 1074-1089 (2005
65. Wu, W. et al. Bayesian population decoding of motor cortical activity using a kalman filter. Neural Comput. 18, 80-118 (2006
66. Pillow, J.W. et al. Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454, 995-999 (2008
67. Shimazaki, H. et al. State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLOS Comput. Biol. 8, e1002385 (2012
68. Abeles, M. et al. Cortical activity flips among quasi-stationary states. Proc. Natl. Acad. Sci. USA 92, 8616-8620 (1995
69. Danoczy, M. & Hahnloser, R. Efficient estimation of hidden state dynamics from spike trains. in Adv. Neural Inf. Process. Syst. 18, 227-234 (2006
70. Kemere, C. et al. Detecting neural-state transitions using hidden markov models for motor cortical prostheses. J. Neurophysiol. 100, 2441-2452 (2008
71. Smith, A.C. & Brown, E.N. Estimating a state-space model from point process observations. Neural Comput. 15, 965-991 (2003
72. Kulkarni, J.E. & Paninski, L. Common-input models for multiple neural spike-train data. Network 18, 375-407 (2007
73. Paninski, L. et al. A new look at state-space models for neural data. J. Comput. Neurosci. 29, 107-126 (2010
74. Macke, J.H. et al. Empirical models of spiking in neural populations. Adv. Neural Inf. Process. Syst. 24, 1350-1358 (2011
75. Buesing, L., Macke, J. & Sahani, M. Spectral learning of linear dynamics from generalized-linear observations with application to neural population data. Adv. Neural Inf. Process. Syst. 25, 1691-1699 (2012
76. Pfau, D. et al. Robust learning of low-dimensional dynamics from large neural ensembles. Adv. Neural Inf. Process. Syst. 26, 2391-2399 (2013
77. Yu, B.M. et al. Extracting dynamical structure embedded in neural activity. Adv. Neural Inf. Process. Syst. 18, 1545-1552 (2006
78. Petreska, B. et al. Dynamical segmentation of single trials from population neural data. Adv. Neural Inf. Process. Syst. 24, 756-764 (2011
79. Tenenbaum, J.B. et al. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319-2323 (2000
80. Roweis, S.T. & Saul, L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323-2326 (2000
81. Boots, B. & Gordon, G. Two-manifold problems with applications to nonlinear system identification. in Proceedings of the 29th International Conference on Machine Learning (eds. Langford, J. & Pineau, J.) 623-630 (Omnipress, New York, 2012
82. Salinas, E. & Abbott, L.F. Vector reconstruction from firing rates. J. Comput. Neurosci. 1, 89-107 (1994
83. Overschee, P.V. & Moor, B.D. Subspace Identification For Linear Systems: Theory, Implementation, Applications (Kluwer Academic Publishers, 1996
84. Diaconis, P. & Freedman, D. Asymptotics of graphical projection pursuit. Ann. Stat. 12, 793-815 (1984
85. Gerstein, G.L. & Perkel, D.H. Simultaneously recorded trains of action potentials: analysis and functional interpretation. Science 164, 828-830 (1969
86. Cohen, M.R. & Kohn, A. Measuring and interpreting neuronal correlations. Nat. Neurosci. 14, 811-819 (2011
87. McCullagh, P. & Nelder, J.A. Generalized Linear Models, vol. 37 (Chapman and Hall, 1998
88. Lawhern, V. et al. Population decoding of motor cortical activity using a generalized linear model with hidden states. J. Neurosci. Methods 189, 267-280 (2010
89. Vidne, M. et al. Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. 33, 97-121 (2012
90. Shlens, J. et al. The structure of multi-neuron firing patterns in primate retina. J. Neurosci. 26, 8254-8266 (2006
91. Schneidman, E. et al. Weak pairwise correlations imply strongly correlated network states in a neural population. Nature 440, 1007-1012 (2006
92. Berkes, P. et al. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science 331, 83-87 (2011
93. Kumar, A., Rotter, S. & Aertsen, A. Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding. Nat. Rev. Neurosci. 11, 615-627 (2010
94. Stanley, G.B., Li, F.F. & Dan, Y. Reconstruction of natural scenes from ensemble responses in the lateral geniculate nucleus. J. Neurosci. 19, 8036-8042 (1999
95. Georgopoulos, A.P., Schwartz, A.B. & Kettner, R.E. Neuronal population coding of movement direction. Science 233, 1416-1419 (1986
96. Gilja, V. et al. A high-performance neural prosthesis enabled by control algorithm design. Nat. Neurosci. 15, 1752-1757 (2012
97. Baeg, E.H. et al. Dynamics of population code for working memory in the prefrontal cortex. Neuron 40, 177-188 (2003
98. Hung, C.P. et al. Fast readout of object identity from macaque inferior temporal cortex. Science 310, 863-866 (2005
99. Quiroga, R.Q. & Panzeri, S. Extracting information from neuronal populations: in formation theory and decoding approaches. Nat. Rev. Neurosci. 10, 173-185 (2009
100. Santhanam, G. et al. Factor-analysis methods for higher-performance neural prostheses. J. Neurophysiol. 102, 1315-1330 (2009