Nowadays we have witnessed an enormous amount of neural data being collected. Neural signals are stochastic and dynamic processes measured in specific neural circuits at various spatiotemporal scales. Development of efficient quantitative tools to characterize these signals and extract information that reveals circuit mechanisms is an important task in computational and statistical neuroscience. In this introductory chapter, we review important concepts and representative applications of statistics, signal processing, and control in neuroscience. Finally, we provide roadmaps for this edited book as well as pointers to the literature and other resources.


  1. Abbott, L. F. (1999). Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Research Bulletin, 50, 303–304.Google Scholar
  2. Agarwal, R., Chen, Z., Kloosterman, F., Wilson, M. A., & Sarma, S. V. (2016). A novel nonparametric approach for neural encoding and decoding models of multimodal receptive fields. Neural Computation, 28, 1356–1387.Google Scholar
  3. Aquino, K., Robinson, P., Schira, M., & Breakspear, M. (2014). Deconvolution of neural dynamics from fMRI data using a spatiotemporal hemodynamic response function. Neuroimage, 94, 203–215.Google Scholar
  4. Ba, D., Babadi, B., Purdon, P. L., & Brown, E. N. (2014). Robust spectrotemporal decomposition by iteratively reweighed least squares. Proceedings of National Academy of Sciences, USA, 111(50), E5336–E5345.Google Scholar
  5. Babadi, B., Obregon-Henao, G., Lamus, C., Hämäläinen, M. S., Brown, E. N., & Purdon, P. L. (2014). A subspace pursuit-based iterative greedy hierarchical solution to the neuromagnetic inverse problem. Neuroimage, 87, 427–443.Google Scholar
  6. Bansal, A. K., Truccolo, W., Vargas-Irwin, C. E., & Donoghue, J. P. (2012). Decoding 3D reach and grasp from hybrid signals in motor and premotor cortices: Spikes, multiunit activity, and local field potentials. Journal of Neurophysiology, 107, 1337–1355.Google Scholar
  7. Barbieri, R., Frank, L. M., Nguyen, D. P., Quirk, M. C., Solo, V., Wilson, M. A., & Brown, E. N. (2004). Dynamic analyses of information encoding in neural ensembles. Neural Computation, 16(2), 277–307.Google Scholar
  8. Benabid, A. L., Chabardes, S., Mitrofanis, J., & Pollak, P. (2009). Deep brain stimulation of the subthalamic nucleus for the treatment of Parkinson’s disease. Lancet Neurology, 8(1), 67–81.Google Scholar
  9. Bialek, W., Rieke, F., de Ruyter van Steveninck, R. R., & Warland, D. (1991). Reading a neural code. Science, 252, 1854–1857.Google Scholar
  10. Brockwell, A. E., Kass, R. E., & Schwartz, A. B. (2007). Statistical signal processing and the motor cortex. Proceedings of the IEEE, 95(5), 891–898.Google Scholar
  11. Brockwell, A. E., Rojas, A. L., & Kass, R. E. (2004). Recursive Bayesian decoding of motor cortical signals by particle filtering. Journal of Neurophysiology, 91(4), 1899–1907.Google Scholar
  12. Brown, E. N. (2005). The theory of point processes for neural systems. In C. Chow, B. Gutkin, D. Hansel, C. Meunier, & J. Dalibard (Eds.), Methods and models in neurophysics (pp. 691–726). Amsterdam: Elsevier.Google Scholar
  13. Brown, E. N., Barbieri, R., Eden, U. T., & Frank, L. M. (2003). Likelihood methods for neural data analysis. In J. Feng (Ed.), Computational neuroscience: A comprehensive approach (pp. 253–286). Boca Raton: CRC Press.Google Scholar
  14. Brown, E. N., Frank, L. M., Tang, D., Quirk, M. C., & Wilson, M. A. (1998). A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells. Journal of Neuroscience, 18, 7411–7425.Google Scholar
  15. Brown, E. N., & Kass, R. E. (2009). What is statistics? The American Statistician, 7, 456–461.Google Scholar
  16. Brown, E. N., Kass, R. E., & Mitra, P. P. (2004). Multiple neural spike train data analysis: State-of-the-art and future challenges. Nature Neuroscience, 7, 456–461.Google Scholar
  17. Brown, E. N., Ngyuen, D. P., Frank, L. M., Wilson, M. A., & Solo, V. (2001). An analysis of neural receptive field plasticity by point process adaptive filtering. Proceedings of National Academy of Sciences USA, 98, 12261–12266.Google Scholar
  18. Brown, E. N., Solo, V., Choe, Y., & Zhang, Z. (2004). Measuring period of human biological clock: Infill asymptotic analysis of harmonic regression parameter estimates. In Methods in enzymology (Vol. 383, pp. 382–405). Amsterdam: Elsevier.Google Scholar
  19. Butson, C. R., & McIntyre, C. C. (2008). Current steering to control the volume of tissue activated during deep brain stimulation. Brain Stimulation, 1(1), 7–15.Google Scholar
  20. Calabrese, A., Schumacher, J. W., Schneider, D. M., Paninski, L., & Woolley, S. M. N. (2011). A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds. PLoS One, 6(1), e16104.Google Scholar
  21. Chase, S. M., Kass, R. E., & Schwartz, A. B. (2012). Behavioral and neural correlates of visuomotor adaptation observed through a brain-computer interface in primary motor cortex. Journal of Neurophysiology, 108(2), 624–644.Google Scholar
  22. Chen, Z. (Ed.) (2015). Advanced state space methods in neural and clinical data. Cambridge: Cambridge University Press.Google Scholar
  23. Chen, Z. (2017). A primer on neural signal processing. IEEE Circuits and Systems Magazine, 17(1), 33–50.Google Scholar
  24. Chen, Z., Barbieri, R., & Brown, E. N. (2010). State-space modeling of neural spike train and behavioral data. In K. Oweiss (Ed.), Statistical signal processing for neuroscience and neurotechnology (pp. 175–218). Amsterdam: Elsevier.Google Scholar
  25. Chen, Z., Gomperts, S. N., Yamamoto, J., & Wilson, M. A. (2014). Neural representation of spatial topology in the rodent hippocampus. Neural Computation, 26(1), 1–39.Google Scholar
  26. Chen, Z., Kloosterman, F., Brown, E. N., & Wilson, M. A. (2012). Uncovering spatial topology represented by rat hippocampal population neuronal codes. Journal of Computational Neuroscience, 33(2), 227–255.Google Scholar
  27. Chen, Z., Kloosterman, F., Layton, S., & Wilson, M. A. (2012). Transductive neural decoding for unsorted neuronal spikes of rat hippocampus. In Proceedings of IEEE Engineering in Medicine and Biology Conference (pp. 1310–1313).Google Scholar
  28. Chen, Z., Putrino, D. F., Ghosh, S., Barbieri, R., & Brown, E. N. (2011). Statistical inference for assessing functional connectivity of neuronal ensembles with sparse spiking data. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 19(2), 121–135.Google Scholar
  29. Ching, S., & Ritt, J. T. (2013). Control strategies for underactuated neural ensembles driven by optogenetic stimulation. Frontiers in Neural Circuits, 7, 54.Google Scholar
  30. Coleman, T. P., & Sarma, S. S. (2010). A computationally efficient method for nonparametric modeling of neural spiking activity with point processes. Neural Computation, 22(8), 2002–2030.Google Scholar
  31. Collinger, J. L., Wodlinger, B., Downey, J. E., Wang, W., Tyler-Kabara, E. C., Weber, D. J., et al. (2013). High-performance neuroprosthetic control by an individual with tetraplegia. The Lancet, 381, 557–564.Google Scholar
  32. Colpan, M. E., Li, Y., Dwyer, J., & Mogul, D. J. (2007). Proportional feedback stimulation for seizure control in rats. Epilepsia, 48(8), 594–603.Google Scholar
  33. Cunningham, J. P., & Yu, B. M. (2014). Dimensionality reduction for large-scale neural recordings. Nature Neuroscience, 17(11), 1500–1509.Google Scholar
  34. Czanner, G., Eden, U. T., Wirth, S., Yanike, M., Suzuki, W. A., & Brown, E. N. (2008). Analysis of between-trial and within-trial neural spiking dynamics. Journal of Neurophysiology, 99(5), 2672–2693.Google Scholar
  35. D’Aleo, R., Rouse, A., Schieber, M., & Sarma, S. V. (2017). An input-output linear time invariant model captures neuronal firing responses to external and behavioral events. In Proceedings of IEEE Engineering in Medicine and Biology Conference.Google Scholar
  36. Deneux, T., Kaszas, A., Szalay, G., Katona, G., Lakner, T., Grinvald, A., et al. (2016). Accurate spike estimation from noisy calcium signals for ultrafast three-dimensional imaging of large neuronal populations in vivo. Nature Communications, 7, 12190.Google Scholar
  37. Deng, X., Liu, D. F., Kay, K., Frank, L. M., & Eden, U. T. (2015). Clusterless decoding of position from multiunit activity using a marked point process filter. Neural Computation, 27(7), 1438–1460.Google Scholar
  38. DiMatteo, I., Genovese, C. R., & Kass, R. E. (2001). Bayesian curve fitting with free-knot splines. Biometrika, 88, 1055–1071.Google Scholar
  39. Donoghue, J. P. (2008). Bridging the brain to the world: A perspective on neural interface systems. Neuron, 60(3), 511–521.Google Scholar
  40. Doucet, A., de Freitas, N., & Gordon, N. (Eds.) (2001). Sequential Monte Carlo methods in practice. New York: Springer.Google Scholar
  41. Eden, U. T., Frank, L. M., Barbieri, R., Solo, V., & Brown, E. N. (2004). Dynamic analysis of neural encoding by point process adaptive filtering. Neural Computation, 16(5), 971–998.Google Scholar
  42. Ehrens, D., Sritharan, D., & Sarma, S. (2015). Closed-loop control of a fragile network: Application to seizure-like dynamics of an epilepsy model. Frontiers in Neuroscience, 9, 58.Google Scholar
  43. Ergun, A., Barbieri, B., Eden, U. T., Wilson, M. A., & Brown, E. N. (2007). Construction of point process adaptive filter algorithms for neural systems using sequential monte carlo methods. IEEE Transactions on Biomedical Engineering, 54(3), 419–428.Google Scholar
  44. Faghih, R. T. (2014). System Identification of Cortisol Secretion: Characterizing Pulsatile Dynamics. Ph.D. thesis. Cambridge: Massachusetts Institute of Technology.Google Scholar
  45. Faghih, R. T., Dahleh, M. A., Adler, G., Klerman, E., & Brown, E. N. (2014). Deconvolution of serum cortisol levels by using compressed sensing. PLoS One, 9(1), e85204.Google Scholar
  46. Faghih, R. T., Dahleh, M. A., Adler, G., Klerman, E., & Brown, E. N. (2015). Quantifying pituitary adrenal dynamics: Deconvolution of concurrent cortisol and adrenocorticotropic hormone data by compressed sensing. IEEE Transactions on Biomedical Engineering, 62(10), 2379–2388.Google Scholar
  47. Faghih, R. T., Dahleh, M. A., & Brown, E. N. (2015). Optimization formulation for characterization of pulsatile cortisol secretion. Frontiers in Neuroscience, 9, 228.Google Scholar
  48. Friedrich, J., Zhou, P., & Paninski, L. (2017). Fast online deconvolution of calcium imaging data. PLoS Computational Biology, 13(3), e1005423.Google Scholar
  49. Gale, J. T., Amirnovin, R., Williams, Z. M., Flaherty, A. W., & Eskandar, E. N. (2008). From symphony to cacophony: Pathophysiology of the human basal ganglia in Parkinson disease. Neuroscience & Biobehavioral Review, 32(3), 378–387.Google Scholar
  50. Gale, J. T., Shields, D. C., Jain, F. A., Amirnovin, R., & Eskandar, E. N. (2009). Subthalamic nucleus discharge patterns during movement in the normal monkey and Parkinsonian patient. Brain Research, 3, 240–245.Google Scholar
  51. Ganguly, K., & Carmena, J. M. (2009). Emergence of a stable cortical map for neuroprosthetic control. PLoS Biology, 7(7), e1000153.Google Scholar
  52. Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2004). Bayesian data analysis (2nd ed.). London: Chapman & Hall/CRC Press.Google Scholar
  53. Georgopoulos, A. P., Schwartz, A. B., & Kettner, R. E. (1986). Neuronal population coding of movement direction. Science, 233, 1416–1419.Google Scholar
  54. Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., et al. (2012). A high-performance neural prosthesis enabled by control algorithm design. Nature Neuroscience, 15, 1752–1757.Google Scholar
  55. Gitelman, R., Penny, W., Ashburner, J., & Friston, K. (2003). Modeling regional and pyschophysiologic interactions in fMRI: The importance of hemodynamic deconvolution. Neuroimage, 19, 200–207.Google Scholar
  56. Gluckman, B. J., Nguyen, H., Weinstein, S. L., & Schiff, S. J. (2001). Adaptive electric field control of epileptic seizures. Journal of Neuroscience, 21(2), 590–600.Google Scholar
  57. Good, L. B., Sabesan, S., Marsh, S. T., Tsakalis, K., Treiman, D., & Iasemidis, L. (2009). Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents. International Journal of Neural Systems, 19(3), 173–196.Google Scholar
  58. Grienberger, C., & Konnerth, A. (2012). Imaging calcium in neurons. Neuron, 73(5), 862–885.Google Scholar
  59. Grosenick, L., Marshel, J. H., & Deisseroth, K. (2015). Closed-loop and activity-guided optogenetic control. Neuron, 86(1), 106–139.Google Scholar
  60. Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.) (2010). Bayesian nonparametrics. Cambridge: Cambridge University Press.Google Scholar
  61. Hochberg, L. R., Bacher, D., Jarosiewicz, B., Masse, N. Y., Simeral, J. D., Vogel, J., et al. (2012). Reach and grasp by people with tetraplegia using a neurally controlled robotic arm. Nature, 485, 372–375.Google Scholar
  62. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative descrip-tion of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117(4), 500–544.Google Scholar
  63. Huys, Q. J. M., Zemel, R. S., Natarajan, R., & Dayan, P. (2007). Fast population coding. Neural Computation, 19, 404–441.Google Scholar
  64. Izhikevich, E. M. (2006). Dynamical systems in neuroscience: The geometry of excitability and bursting. Cambridge: MIT Press.Google Scholar
  65. Jarosiewicz, B., Chase, S. M., Fraser, G. W., Velliste, M., Kass, R. E., & Schwartz, A. B. (2008). Functional network reorganization during learning in a brain-computer interface paradigm. Proceedings of the National Academy of Sciences USA, 105(49), 19486–19491.Google Scholar
  66. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME–Journal of Basic Engineering, 82, 35–45.Google Scholar
  67. Kass, R. E., Eden, U. T., & Brown, E. N. (2014). Analysis of neural data. New York: Springer.Google Scholar
  68. Kass, R. E., & Ventura, V. (2001). A spike-train probability model. Neural Computation, 13(8), 1713–1720.Google Scholar
  69. Kass, R. E., Ventura, V., & Brown, E. N. (2005). Statistical issues in the analysis of neuronal data. Journal of Neurophysiology, 94, 8–25.Google Scholar
  70. Kim, S., Putrino, D., Ghosh, S., & Brown, E. N. (2011). A granger causality measure for point process models of ensemble neural spiking activity. PLoS Computational Biology, 7(3), e1001110.Google Scholar
  71. Kloosterman, F., Layton, S., Chen, Z., & Wilson, M. A. (2014). Bayesian decoding of unsorted spikes in the rat hippocampus. Journal of Neurophysiology, 111(1), 217–227.Google Scholar
  72. Knight, B. W. (1972). Dynamics of encoding in a population of neurons. Journal of General Physiology, 59, 734–766.Google Scholar
  73. Kobak, D., Brendel, W., Constantinidis, C., Feierstein, C. E., Kepecs, A., Mainen, Z. F., et al. (2016). Demixed principal component analysis of neural population data. eLife, 5, e10989.Google Scholar
  74. Krishnaswamy, P., Bonmassar, G., Poulsen, C., Pierce, E. T., Purdon, P. L., & Brown, E. N. (2016). Reference-free removal of EEG-fMRI ballistocardiogram artifacts with harmonic regression. NeuroImage, 128, 398–412.Google Scholar
  75. Kühn, A. A., Tsui, A., Aziz, T., Ray, N., Brücke, C., Kupsch, A., et al. (2009). Pathological synchronisation in the subthalamic nucleus of patients with parkinson’s disease relates to both bradykinesia and rigidity. Experimental Neurology, 215, 380–387.Google Scholar
  76. Kuncel, A. M., Cooper, S. E., Wolgamuth, B. R., Clyde, M. A., Snyder, S. A., Montgomery, E. B. J., et al. (2006). Clinical response to varying the stimulus parameters in deep brain stimulation for essential tremor. Movement Disorder, 21, 1920–1928.Google Scholar
  77. Lamus, C., Hamalainen, M. S., Temereanca, S., Long, C. J., Brown, E. N., & Purdon, P. L. (2012). A spatiotemporal dynamic distributed solution to the MEG inverse problem. NeuroImage, 63(2), 894–909.Google Scholar
  78. Lang, A. E., & Lozano, A. M. (1998). Parkinson’s disease. First of two parts. New England Journal of Medicine, 15, 1044–1053.Google Scholar
  79. Lawhern, V., Wu, W., Hatsopoulos, N. G., & Paninski, L. (2010). Population decoding of motor cortical activity using a generalized linear model with hidden states. Journal of Neuroscience Methods, 189, 267–280.Google Scholar
  80. Lebedev, M. A., & Nicolelis, M. A. (2006). Brain-machine interfaces: Past, present and future. Trends in Neurosciences, 29(9), 536–546.Google Scholar
  81. Lewicki, M. S. (1998). A review of methods for spike sorting: The detection and classification of neural action potentials. Network, 9(4), R53–R78.Google Scholar
  82. Lewis, L. D., Setsompop, K., Rosen, B. R., & Polimeni, J. R. (2016). Fast fMRI can detect oscillatory neural activity in humans. Proceedings of National Academy of Sciences, USA, 113, E6679–E6685.Google Scholar
  83. Li, X., Chen, Q., & Xue, F. (2017). Biological modelling of a computational spiking neural network with neuronal avalanches. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 375(2096), 1–16.Google Scholar
  84. Malik, W. Q., Schummers, J., Sur, M., & Brown, E. N. (2011). Denoising two-photon calcium imaging data. PLoS One, 6(6), e20490.Google Scholar
  85. Montgomery, E. B., & Gale, J. T. (2002). Deep brain stimulation for parkinsons disease: Disrupting the disruption. Lancet Neurology, 1, 225–231.Google Scholar
  86. Montgomery, E. B., & Gale, J. T. (2008). Mechanisms of action of deep brain stimulation (DBS). Neuroscience & Biobehavioral Review, 32, 388–407.Google Scholar
  87. Nandi, A., Kafashan, M., & Ching, S. (2017). Control analysis and design for statistical models of spiking networks. IEEE Transactions on Control of Network Systems, in press.
  88. Okatan, M., Wilson, M., & Brown, E. (2005). Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity. Neural Computation, 17, 1927–1961.Google Scholar
  89. Onativia, J., Schultz, S. R., & Dragotti, P. L. (2013). A finite rate of innovation algorithm for fast and accurate spike detection from two-photon calcium imaging. Journal of Neural Engineering, 10, 046017.Google Scholar
  90. Orsborn, A. L., Dangi, S., Moorman, H. G., & Carmena, J. M. (2012). Closed-loop decoder adaptation on intermediate time-scales facilitates rapid bmi performance improvements independent of decoder initialization conditions. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), 468–477.Google Scholar
  91. Pascual-Marqui, R. D. (1999). Review of methods for solving the EEG inverse problem. International Journal of Bioelectromagnetism, 1(1), 75–86.Google Scholar
  92. Pawitan, Y. (2001). In all likelihood: Statistical modelling and inference using likelihood. Gloucestershire: Clarendon Press.Google Scholar
  93. Penny, W., Ghahramani, Z., & Friston, K. (2005). Bilinear dynamical systems. Philosophical Transactions on Royal Society of London B, 360, 983–993.Google Scholar
  94. Perkel, D. H., & Bullock, T. H. (1968). Neural coding: By Donald H. Perkel and Theodore Holmes Bullock. Neurosciences Research Program (NRP).Google Scholar
  95. Perlmutter, J. S., & Mink, J. W. (2006). Deep brain stimulation. Annual Review in Neuroscience, 29, 229–257.CrossRefGoogle Scholar
  96. Pnevmatikakis, E. A., Soudry, D., Gao, Y., Machado, T. A., Merel, J., Pfau, D., et al. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron, 89(2), 285–299.CrossRefGoogle Scholar
  97. Prerau, M. J., Smith, A. C., Eden, U. T., Kubota, Y., Yanike, M., Suzuki, W., et al. (2009). Characterizing learning by simultaneous analysis of continuous and binary measures of performance. Journal of Neurophysiology, 102(5), 3060–3072.CrossRefGoogle Scholar
  98. Rahmati, V., Kirmse, K., Marković, D., Holthoff, K., & Kiebel, S. J. (2016). Inferring neuronal dynamics from calcium imaging data using biophysical models and Bayesian inference. Nature Communications, 12(3), e1004835.Google Scholar
  99. Ressler, K. J., & Mayberg, H. (2007). Targeting abnormal neural circuits in mood and anxiety disorders: From the laboratory to the clinic. Nature Neuroscience, 10, 1116–1124.CrossRefGoogle Scholar
  100. Rieke, F., Warland, D., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge: MIT Press.zbMATHGoogle Scholar
  101. Ringach, D., & Shapley, R. (2004). Reverse correlation in neurophysiology. Cognitive Science, 28, 147–166.CrossRefGoogle Scholar
  102. Robert, C. P. (2007). The Bayesian choice: From decision-theoretic foundations to computational implementation (2nd ed.). New York: Springer.zbMATHGoogle Scholar
  103. Romano, S. A., Prez-Schuster, V., Jouary, A., Boulanger-Weill, J., Candeo, A., Pietri, T., et al. (2017). An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics. PLoS Computational Biology, 13(6), e1005526.CrossRefGoogle Scholar
  104. Santaniello, S., Montgomery, E. B., Gale, J. T., & Sarma, S. V. (2012). Non-stationary discharge patterns in motor cortex under subthalamic nucleus deep brain stimulation: A review. Frontiers in Integrative Neuroscience, 6, 35.CrossRefGoogle Scholar
  105. Sarma, S. V., Cheng, M. L., Eden, U. T., Williams, Z., Brown, E. N., & Eskandar, E. N. (2012). The effects of cues on neurons in the basal ganglia in Parkinson’s disease. Frontiers in Integrative Neuroscience, 6, 40.CrossRefGoogle Scholar
  106. Schliebs, S., & Kasabov, N. (2014). Computational modeling with spiking neural networks. In N. Kasabov (Ed.), Springer handbook of bio-/neuroinformatics (pp. 625–646). Berlin: Springer.CrossRefGoogle Scholar
  107. Schwartz, A. B., Cui, X. T., Weber, D. J., & Moran, D. W. (2006). Brain-controlled interfaces: Movement restoration with neural prosthetics. Neuron, 52(1), 205–220.CrossRefGoogle Scholar
  108. Shanechi, M. M. (2017). Brain-machine interface control algorithms. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25(10), 1725–1734.CrossRefGoogle Scholar
  109. Shanechi, M. M., Chemali, J. J., Liberman, M., Solt, K., & Brown, E. N. (2013). A brain-machine interface for control of medically-induced coma. PLoS Computational Biology, 9(10), e1003284.CrossRefGoogle Scholar
  110. Shanechi, M. M., Hu, R. C., Powers, M., Wornell, G. W., Brown, E. N., & Williams, Z. M. (2012). Neural population partitioning and a concurrent brain-machine interface for sequential motor function. Nature Neuroscience, 15(12), 1715–1722.CrossRefGoogle Scholar
  111. Shanechi, M. M., Orsborn, A. L., & Carmena, J. M. (2016). Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering. PLoS Computational Biology, 12(4), e1004730.CrossRefGoogle Scholar
  112. Shanechi, M. M., Orsborn, A. L., Moorman, H. G., Gowda, S., Dangi, S., & Carmena, J. M. (2017). Rapid control and feedback rates enhance neuroprosthetic control. Nature Communications, 8, 13825.CrossRefGoogle Scholar
  113. Shanechi, M. M., Williams, Z. M., Wornell, G. W., Hu, R., Powers, M., & Brown, E. N. (2013). A real-time brain-machine interface combining motor target and trajectory intent using an optimal feedback control design. PLoS One, 8(4), e59049.CrossRefGoogle Scholar
  114. Shanechi, M. M., Wornell, G. W., Williams, Z. M., & Brown, E. N. (2013). Feedback-controlled parallel point process filter for estimation of goal-directed movements from neural signals. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 21, 129–140.CrossRefGoogle Scholar
  115. Shenoy, K. V., Sahani, M., & Churchland, M. M. (2013). Cortical control of arm movements: A dynamical systems perspective. Annual Review of Neuroscience, 36, 337–359.CrossRefGoogle Scholar
  116. Shimazaki, H., Amari, S., Brown, E. N., & Gruen, S. (2012). State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data. PLoS Computational Biology, 8(3), e1002385.MathSciNetCrossRefGoogle Scholar
  117. Smith, A. C., & Brown, E. N. (2003). Estimating a state-space model from point process observations. Neural Computation, 15(5), 965–991.CrossRefGoogle Scholar
  118. Smith, A. C., Frank, L. M., Wirth, S., Yanike, M., Hu, D., Kubota, Y., et al. (2004). Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience, 24, 447–461.CrossRefGoogle Scholar
  119. Smith, A. C., Stefani, M. R., Moghaddam, B., & Brown, E. N. (2005). Analysis and design of behavioral experiments to characterize population learning. Journal of Neurophysiology, 93, 1776–1792.CrossRefGoogle Scholar
  120. Smith, A. C., Wirth, S., Suzuki, W. A., & Brown, E. N. (2007). Bayesian analysis of interleaved learning and response bias in behavioral experiments. Journal of Neurophysiology, 97, 2516–2524.CrossRefGoogle Scholar
  121. Sohal, V. S., & Sun, F. T. (2011). Responsive neurostimulation suppresses synchronized cortical rhythms in patients with epilepsy. Neurosurgery Clinics of North America, 22(4), 481–488.CrossRefGoogle Scholar
  122. Srinivasan, L., Eden, U. T., Willsky, A. S., & Brown, E. N. (2006). A state-space analysis for reconstruction of goal-directed movements using neural signals. Neural Computation, 18, 2465–2494.MathSciNetCrossRefGoogle Scholar
  123. Stavisky, S. D., Kao, J. C., Nuyujukian, P., Ryu, S. I., & Shenoy, K. V. (2015). A high performing brain-machine interface driven by low-frequency local field potentials alone and together with spikes. Journal of Neural Engineering, 12, 036009.CrossRefGoogle Scholar
  124. Stevenson, I. H., & Kording, K. P. (2011). How advances in neural recording affect data analysis. Nature Neuroscience, 14, 139–142.CrossRefGoogle Scholar
  125. Stevenson, I. H., London, B. M., Oby, E. R., Sachs, N. A., Reimer, J., Englitz, B., et al. (2009). Bayesian inference of functional connectivity and network structure from spikes. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17, 203–213.CrossRefGoogle Scholar
  126. Stokes, P. A., & Purdon, P. L. (2017). A study of problems encountered in Granger causality analysis from a neuroscience perspective. Proceedings of National Academy of Sciences, USA, 114(34), E7063–E7072.MathSciNetCrossRefGoogle Scholar
  127. Taylor, D. M., Tillery, S. I. H., & Schwartz, A. B. (2002). Direct cortical control of 3D neuroprosthetic devices. Science, 296, 1829–1832.CrossRefGoogle Scholar
  128. Thakor, N. V. (2013). Translating the brain-machine interface. Science Translational Medicine, 5, 210–217.CrossRefGoogle Scholar
  129. Theis, L., Berens, P., Froudarakis, E., Reimer, J., Rosn, M. R., Baden, T., et al. (2016). Benchmarking spike rate inference in population calcium imaging. Neuron, 90(3), 471–482.CrossRefGoogle Scholar
  130. Tommasi, G., Lanotte, M., Albert, U., Zibetti, M., Castelli, L., Maina, G. et al. (2008). Transient acute depressive state induced by subthalamic region stimulation. Journal of Neurological Sciences, 273, 135–138.CrossRefGoogle Scholar
  131. Truccolo, W., & Donoghue, J. P. (2007). Nonparametric modeling of neural point processes via stochastic gradient boosting regression. Neural Computation, 19(3), 672–705.CrossRefGoogle Scholar
  132. 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(2), 1074–1089.CrossRefGoogle Scholar
  133. Truccolo, W., Fiehs, G. M., Donoghue, J. P., & Hochberg, L. R. (2008). Primary motor cortex tuning to intended movement kinematics in humans with tetraplegia. Journal of Neuroscience, 28(5), 1163–1178.CrossRefGoogle Scholar
  134. Velliste, M., Perel, S., Spalding, M. C., Whitford, A. S., & Schwartz, A. B. (2008). Cortical control of a prosthetic arm for self-feeding. Nature, 453, 1098–1101.CrossRefGoogle Scholar
  135. Ventura, V. (2008). Spike train decoding without spike sorting. Neural Computation, 20(4), 923–963.MathSciNetCrossRefGoogle Scholar
  136. Ventura, V. (2009). Traditional waveform based spike sorting yields biased rate code estimates. Proceedings of National Academy of Science, USA, 106, 6921–6926.Google Scholar
  137. Vogelstein, J., Packer, A., Machado, T. A., Sippy, T., Babadi, B., Yuste, R., & Paninski, L. (2010). Fast nonnegative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology, 104, 3691–3704.CrossRefGoogle Scholar
  138. Vogelstein, J., Watson, B., Packer, A., Yuste, R., Jedynak, B., & Paninski, L. (2009). Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal, 97(2), 636–655.CrossRefGoogle Scholar
  139. Wei, X. F., & Grill, W. M. (2009). Impedance characteristics of deep brain stimulation electrodes in vitro and in vivo. Journal of Neural Engineering, 6, 046008.CrossRefGoogle Scholar
  140. Wendel, K., Väisämen, O., Malmivuo, J., Gencer, N. G., Vanrumste, B., Durka, P., et al. (2009). EEG/MEG source imaging: Methods, challenges, and open issues. Computational Intelligence and Neuroscience, 2009, 656092.CrossRefGoogle Scholar
  141. Wichmann, T., & DeLong, M. (2006). Deep brain stimulation for neurologic and neuropsychiatric disorders. Neuron, 52(1), 197–204.CrossRefGoogle Scholar
  142. Willett, F. R., Suminski, A. J., Fagg, A. H., & Hatsopoulos, N. G. (2013). Improving brain-machine interface performance by decoding intended future movements. Journal of Neural Engineering, 10(2), 026011.CrossRefGoogle Scholar
  143. Wirth, S., Yanike, M., Frank, L. M., Smith, A. C., Brown, E. N., & Suzuki, W. A. (2003). Single neurons in the monkey hippocampus and learning of new associations. Science, 300, 1578–1584.CrossRefGoogle Scholar
  144. Wong, K. F. K., Smith, A. C., Pierce, E. T., Harrell, P. G., Walsh, J. L., Salazar-Gomez, A. F., et al. (2014). Statistical modeling of behavioral dynamics during propofol-induced loss of consciousness. Journal of Neuroscience Methods, 227, 65–74.CrossRefGoogle Scholar
  145. Wu, W., Gao, Y., Bienenstock, E., Donoghue, J. P., & Black, M. J. (2006). Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Computation, 18(1), 80–118.MathSciNetCrossRefGoogle Scholar
  146. Wu, W., Kulkarni, J. E., Hatsopoulos, N. G., & Paninski, L. (2009). Neural decoding of hand motion using a linear state-space model with hidden states. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17, 370–378.CrossRefGoogle Scholar
  147. Wu, W., Nagarajan, S., & Chen, Z. (2016). Bayesian machine learning: EEG/MEG signal processing measurements. IEEE Signal Processing Magazine, 33(1), 14–36.CrossRefGoogle Scholar
  148. Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V., & Sahani, M. (2009). Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Journal of Neurophysiology, 102(1), 614–635.CrossRefGoogle Scholar
  149. Zahodne, L. B., Young, S., Darrow, L. K., Nisenzon, A., Fernandez, H. H., Okun, M. S., et al. (2009). Examination of the lille apathy rating scale in Parkinson disease. Movement Disorder, 24(5), 677–683.CrossRefGoogle Scholar
  150. Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10(2), 403–430.CrossRefGoogle Scholar
  151. Zhang, K., Ginzburg, I., McNaughton, B. L., & Sejnowski, T. J. (1998). Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells. Journal of Neurophysiology, 79(2), 1017–1044.CrossRefGoogle Scholar
  152. Zhou, B., Moorman, D., Behseta, S., Ombao, H., & Shahbaba, B. (2016). A dynamic bayesian model for characterizing cross-neuronal interactions during decision making. Journal of American Statistical Association, 111, 459–471.MathSciNetCrossRefGoogle Scholar
  153. Zhuang, J., Truccolo, W., Vargas-Irwin, C., & Donoghue, J. P. (2009). Decoding 3-D reach and grasp kinematics from high-frequency local field potentials in primate primary motor cortex. IEEE Transactions on Biomedical Engineering, 57(7), 1774–1784.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.New York University School of MedicineNew YorkUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA

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