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Research Article

Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains

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Pages 75-98 | Received 15 Aug 2012, Accepted 21 Mar 2013, Published online: 06 Jun 2013

References

  • Ahmadian Y, Pillow J, Paninski L. Efficient Markov Chain Monte Carlo methods for decoding population spike trains. Neural Computation 2011; 23: 46–96
  • Aldworth Z, Miller J, Gedeon T, Cummins G, Dimitrov A. Dejittered spike-conditioned stimulus waveforms yield improved estimates of neuronal feature selectivity and spike-timing precision of sensory interneurons. Journal of Neuroscience 2005; 25: 5323–5332
  • Amarasingham A, Harrison MT, Hatsopoulos NG, Geman S. Conditional modeling and the jitter method of spike re-sampling. Journal of Neurophysiology 2012; 107: 517–531
  • Calabrese A, Paninski L. Kalman filter mixture model for spike sorting of non-stationary data. Journal of neuroscience methods 2011; 196: 159–169
  • Casella G, Berger R, 2001. Statistical Inference. Duxbury Press
  • Chen Z, Kloosterman F, Layton S, Wilson MA, 2012. Transductive neural decoding for unsorted neuronal spikes of rat hippocampus. In Proc. IEEE EMBC ‘12, pp. 1310–1313, San Diego, CA
  • Chen Z, Vijayan S, Barbieri R, Wilson MA, Brown EN. Discrete-and continuous-time probabilistic models and algorithms for inferring neuronal up and down states. Neural Computation 2009; 21: 1797–1862
  • Cossart R, Aronov D, Yuste R. Attractor dynamics of network up states in the neocortex. Nature 2003; 423: 283–288
  • Escola S, Fontanini A, Katz D, Paninski L. Hidden Markov models for the stimulus-response relationships of multistate neural systems. Neural Computation 2011; 23: 1071–1132
  • Faisal A, Laughlin S. Stochastic simulations on the reliability of action potential propagation in thin axons. PLoS Comput Biol 2007; 3(5)e79
  • Faisal AA, White JA, Laughlin SB. Ion-channel noise places limits on the miniaturization of the brains wiring. Curr Bio 2005; 15: 1143–1149
  • Fruhwirth-Schnatter S, 2006. Finite Mixture and Markov Switching Models. Springer
  • Goldwyn JH, Shea-Brown E, Rubinstein JT. Encoding and decoding amplitude-modulated cochlear implant stimuli - a point process analysis. Journal of Computational Neuroscience 2010; 28(3)405–424
  • Gollisch T. Estimating receptive fields in the presence of spike-time jitter. Network 2006; 17(2)103–129
  • Goodman IN, Johnson DH, 2008. Information theoretic bounds on neural prosthesis effectiveness: The importance of spike sorting. In Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on
  • Haslinger R, Klinkner K, Shalizi C. The computational structure of spike trains. Neural Computation 2010; 22: 121–157
  • Herbst JA, Gammeter S, Ferrero D, Hahnloser R. Spike sorting with hidden markov models. Journal of Neuroscience Methods 2008; 174(1)126–134
  • Hill DN, Mehta SB, Kleinfeld D. Quality metrics to accompany spike sorting of extracellular signals. Journal of Neuroscience 2011; 31(24)8699–8705
  • Horn RA. Topics in matrix analysis. Cambridge University Press, New York, NYUSA 1986
  • Kass R, Ventura V, Brown E. Statistical issues in the analysis of neuronal data. Journal of Neurophysiology 2005; 94: 8–25
  • Lewicki M. A review of methods for spike sorting: The detection and classification of neural action potentials. Network: Computation in Neural Systems 1998; 9: R53–R78
  • Mishchenko Y, Paninski L. Efficient methods for sampling spike trains in networks of coupled neurons. Annals of Applied Statistics 2011; 5: 1893–1919
  • Mishchenko Y, Vogelstein J, Paninski L. A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data. Annals of Applied Statistics 2011; 5(2B)1229–1261
  • Naud R, Gerstner W. Coding and decoding with adapting neurons: A population approach to the peri-stimulus time histogram. Plos Computational Biology 2012; 8(10)e1002711
  • Nossenson N, Messer H. Optimal sequential detection of stimuli from multi-unit recordings taken in densely populated brain regions. Neural Computation 2011; 24(4)895–938
  • Ohki K, Chung S, Ch'ng Y, Kara P, Reid C. Functional imaging with cellular resolution reveals precise micro-architecture in visual cortex. Nature 2005; 433: 597–603
  • Paninski L, Pillow J, Lewi J. Statistical models for neural encoding, decoding, and optimal stimulus design. Progress in Brain Research 2007; 165: 493–507
  • Pillow J, Ahmadian Y, Paninski L. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Computation 2011; 23(1)1–45
  • Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 1989; 77(2)257–286
  • Robert CP, Casella G. Monte Carlo Statistical Methods (Springer Texts in Statistics). Springer-Verlag New York, Inc, Secaucus, NJUSA 2005
  • Sahani M, 1999. Latent variable models for neural data analysis. PhD thesis, California Institute of Technology
  • Smith C, Wood F, Paninski L, 2012. Low rank continuous-space graphical models. In Proceedings of 15th International Conference on Artificial Intelligence and Statistics (AISTATS), pp 1064–1072
  • Tokdar S, Xi P, Kelly RC, Kass RE. Detection of bursts in extracellular spike trains using hidden semi-markov point process models. Journal of Computational Neuroscience 2010; 29: 203–212
  • Toyoizumi T, Rad K, Paninski L. Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness. Neural computation 2009; 21(5)1203–1243
  • Truccolo W, Eden U, Fellows M, Donoghue J, Brown E. A point process framework for relating neural spiking activity to spiking history, neural ensemble and extrinsic covariate effects. Journal of Neurophysiology 2005; 93: 1074–1089
  • Ventura V. Spike train decoding without spike sorting. Neural computation 2008; 20(4)923–963
  • Ventura V. Automatic spike sorting using tuning information. Neural Computation 2009; 21: 2466–2501
  • Vidne M, Ahmadian Y, Shlens J, Pillow J, Kulkarni J, Litke A, Chichilnisky E, Simoncelli E, Paninski L. Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. Journal of Computational Neuroscience 2012; 33: 97–121
  • Wood F, Black M. A nonparametric Bayesian alternative to spike sorting. Journal of Neuroscience Methods 2008; 173: 1–12

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