The computational structure of spike trains

Neural Computation

Volumn 22, Issue 1, 2010, Pages 121-157

  Haslinger, Robert ^a,b   Klinkner, Kristina Lisa ^c   Shalizi, Cosma Rohilla ^c,d  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c CARNEGIE MELLON UNIVERSITY   (United States)

^d SANTA FE INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL NEURAL NETWORK; BRAIN; COMPUTER SIMULATION; ENTROPY; NERVE CELL; NERVE CELL NETWORK; NERVE TRACT; NORMAL DISTRIBUTION; PHYSIOLOGY; PROBABILITY; RAT; SENSORY RECEPTOR; SIGNAL PROCESSING; SOMATOSENSORY CORTEX; STATISTICS; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; BRAIN; COMPUTER SIMULATION; ENTROPY; MARKOV CHAINS; NERVE NET; NEURAL NETWORKS (COMPUTER); NEURAL PATHWAYS; NEURONS; NORMAL DISTRIBUTION; RATS; SENSORY RECEPTOR CELLS; SIGNAL PROCESSING, COMPUTER-ASSISTED; SOMATOSENSORY CORTEX; STOCHASTIC PROCESSES; SYNAPTIC TRANSMISSION;

EID: 77649245604     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/neco.2009.12-07-678     Document Type: Letter

References (44)

1. Abeles, M., Bergman, H., Gat, I., Meilijson, I., Seidemann, E., Tishby, N., et al. (1995). Cortical activity flips among quasi-stationary states. Proc. Natl. Acad. Sci. USA., 92, 8616-8620.
2. Achtman,N., Afshar, A., Santhanam,G., Yu, B.M., Ryu,S.I. ,&Shenoy, K.V. (2007). Free paced high-performance brain-computer interfaces. Journal of Neural Engineering, 4, 336-347.
3. Ahmed, N. U. (1998). Linear and nonlinear filtering for scientists and engineers. Singapore: World Scientific.
4. Amigo, J. M., Szczepanski, J., Wajnryb, E., & Sanchez-Vives, M. V. (2002). On the number of states of the neuronal sources. Biosystems, 68, 57-66.
5. Amigo, J. M., Szczepanski, J., Wajnryb, E., & Sanchez-Vives, M. V. (2004). Estimating the entropy rate of spike trains via Lempel-Ziv complexity. Neural Computation, 16, 717-736.
6. Andermann, M. L., & Moore, C. I. (2006). A sub-columnar direction map in rat barrel cortex. Nature Neuroscience, 9, 543-551.
7. Badii, R., & Politi, A. (1997). Complexity: Hierarchical structures and scaling in physics. Cambridge: Cambridge University Press.
8. Bernardo, J. M., & Smith, A. F. M. (1994). Bayesian theory. New York: Wiley.
9. Brown, E. N., Barbieri, R., Ventura, V., Kass, R. E., & Frank, L. M. (2002). The time rescaling theorem and its application to neural spike train data analysis. Neural Computation, 14, 325-346.
10. 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.
11. Chen, Z., Vijayan, S., Barbieri, R., Wilson, M. A., & Brown, E. N. (2008). Discrete-and continuous-time probablistic models and inference algorithms for neuronal decoding of Up and Down states. Neural Computation, 21, 1797-1862.
12. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory.NewYork: Wiley.
13. Crutchfield, J. P., & Young, K. (1989). Inferring statistical complexity. Physical Review Letters, 63, 105-108.
14. Csiszár, I., & Talata, Z. (2006). Context tree estimation for not necessarily finite memory processes, via BIC and MDL. IEEE Transactions on Information Theory, 52, 1007-1016.
15. Danoczy, M. G., & Hahnloser, R. H. R. (2004). Efficient estimation of hidden state dynamics. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in neural information processing systems, (pp. 227-234). Cambridge, MA: MIT Press.
16. 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, 971-998.
17. Gács, P., Tromp, J. T., & Vitanyi, P. M. B. (2001). Algorithmic statistics. IEEE Transactions on Information Theory, 47, 2443-2463.
18. Gorse, D., & Taylor, J. G. (1990). A general model of stochastic neural processing. Biological Cybernetics, 63, 299-306.
19. Grassberger, P. (1986). Toward a quantitative theory of self-generated complexity. International Journal of Theoretical Physics, 25, 907-938.
20. Gray, R. M. (1988). Probability, random processes, and ergodic properties.NewYork: Springer-Verlag.
21. Jaeger, H. (2000). Observable operator models for discrete stochastic time series. Neural Computation, 12, 1371-1398.
22. Jimenez-Montano, M. A., Ebeling, W., Pohl, T., & Rapp, P. E. (2002). Entropy and complexity of finite sequences and fluctuating quantities. Biosystems, 64, 23-32.
23. Jones, L. M., Fontanini, A., Sadacca, B. F., & Katz, D. B. (2007). Natural stimuli evoke dynamic sequences of states in sensory cortical ensembles. Proc. Natl. Acad. Sci. USA, 104, 18772-18777.
24. Kennel, M. B., & Mees, A. I. (2002). Context-tree modeling of observed symbolic dynamics. Physical Review E, 66, 056209.
25. Klinkner, K. L., Rinaldo, A., & Shalizi, C. R. (2009). Adaptive nonparametric prediction and bootstrapping of discrete time series. Unpublished manuscript.
26. Klinkner, K. L., & Shalizi, C. R. (2009). CSSR: A nonparametric algorithm for predicting and classifying time series. Unpublished manuscript.
27. Klinkner, K. L., Shalizi, C. R., & Camperi, M. F. (2006). Measuring shared information and coordinated activity in neuronal networks. In Y. Weiss, B. Schölkopf, & J. C. Platt (Eds.), Advances in neural information processing systems, 18, (pp. 667-674). Cambridge, MA: MIT Press.
28. Knight, F. B. (1975). A predictive view of continuous time processes. Annals of Probability, 3, 573-596.
29. Littman, M. L., Sutton, R. S., & Singh, S. (2002). Predictive representations of state. In T. G. Dietterich, S. Becker, & Z. Ghahramani (Eds.), Advances in neural information processing systems, 14 (pp. 1555-1561). Cambridge, MA: MIT Press.
30. Löhr, W., & Ay, N. (2009). On the generative nature of prediction. Advances in Complex Systems, 12, 169-194.
31. Marton, K., & Shields, P. C. (1994). Entropy and the consistent estimation of joint distributions. Annals of Probability, 22, 960-977. Correction, Annals of Probability, 24 (1996), 541-545.
32. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115-133.
33. Olufsen, M. S., Whittington, M. A., Camperi, M., & Kopell, N. (2003). New roles for the gamma rhythm: Population tuning and processing for the beta rhythm. Journal of Computation Neuroscience, 14, 33-54.
34. Rapp, P. E., Zimmerman, I. D., Vining, E. P., Cohen, N., Albano, A. M., & Jimenez-Montano, M. A. (1994). The algorithmic complexity of neural spike trains increases during focal seizures. Journal of Neuroscience, 14, 4731-4739.
35. Rieke, F., Warland, D., de Ruyter van Steveninck, R., & Bialek, W. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
36. Shalizi, C. R. (2001). Causal architecture, complexity and self-organization in time series and cellular automata. Unpublished doctoral dissertation, University of Wisconsin.
37. Shalizi, C. R., & Crutchfield, J. P. (2001). Computational mechanics: Pattern and prediction, structure and simplicity. Journal of Statistical Physics, 104, 817-879.
38. Shalizi, C. R., & Klinkner, K. L. (2004). Blind construction of optimal nonlinear recursive predictors for discrete sequences. In M. Chickering & J. Y. Halpern (Eds.), Uncertainty in artificial intelligence: Proceedings of the Twentieth Conference (UAI 2004) (pp. 504-511). Arlington, VA: AUAI Press.
39. Shalizi, C. R., Klinkner, K. L., & Haslinger, R. (2004). Quantifying self-organization with optimal predictors. Physical Review Letters, 93, 118701.
40. Singh, S., Littman, M. L., Jong, N. K., Pardoe, D., & Stone, P. (2003). Learning predictive state representations. In T. Fawcett & N. Mishra (Eds.), Proceedings of the Twentieth International Conference on Machine Learning, (pp. 712-719). New York: AAAI Press.
41. 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.
42. Srinivasan, L., Eden, U. T., Mitter, S. K., & Brown, E. N. (2007). General purpose filter design for neural prosthetic devices. Journal of Neurophysiology, 98, 2456-2475.
43. Szczepanski, J., Amigo, J. M., Wajnryb, E., & Sanchez-Vives, M. V. (2004). Characterizing spike trains with Lempel-Ziv complexity. Neurocomputing, 58, 79-84.
44. Truccolo, W., Eden, U. T., Fellow, M. R., Donoghue, J. P., & Brown, E. N. (2005). A point process framework for relating neural spiking activity to spiking history, neural ensemble and covariate effects. Journal of Neurophysiology, 93, 1074-1089.