Construction of point process adaptive filter algorithms for neural systems using sequential Monte Carlo methods

IEEE Transactions on Biomedical Engineering

Volumn 54, Issue 3, 2007, Pages 419-428

  Ergün, Ayla ^a,b   Barbieri, Riccardo ^a   Eden, Uri T ^a,c   Wilson, Matthew A ^d   Brown, Emery N ^a,d  

^a MASSACHUSETTS GENERAL HOSPITAL   (United States)

^b Boston University   (United States)

^c Boston University   (United States)

^d MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Adaptive filtering; Hidden Markov models; Point processes; Sequential Monte Carlo; State estimation

Indexed keywords

ADAPTIVE FILTERING; COMPUTER SIMULATION; HIDDEN MARKOV MODELS; MONTE CARLO METHODS; NEURAL NETWORKS; PLASTICITY; PROBABILITY DENSITY FUNCTION; STATE ESTIMATION;

ADAPTIVE FILTER ALGORITHMS; STEEPEST DESCENT POINT PROCESS FILTER (SDPPF); STOCHASTIC STATE POINT PROCESS FILTER (SSPPF);

ADAPTIVE ALGORITHMS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; BRAIN DEPTH RECORDING; FILTER; FORAGING; HIPPOCAMPUS; MONTE CARLO METHOD; PROBABILITY; RECEPTIVE FIELD; SIMULATION; SPIKE WAVE; STIMULUS RESPONSE; STOCHASTIC MODEL; THEORETICAL MODEL;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; HIPPOCAMPUS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; MONTE CARLO METHOD; NERVE NET; NEURONAL PLASTICITY; RATS; SIGNAL PROCESSING, COMPUTER-ASSISTED; STOCHASTIC PROCESSES;

EID: 33847716709     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2006.888821     Document Type: Article

References (50)

1. J. H. Kaas, M. M. Merzenich, and H. P. Killackey, "The reorganization of somatosensory cortex following peripheral nerve damage in adult and developing mammals," Annu. Rev. Neurosci., vol. 6, pp. 325-356, 1983.
2. M. M. Merzenich, R. J. Nelson, M. P. Stryker, M. S. Cynader, A. Schoppmann, and J. M. Zook, "Somatosensory cortical map changes following digit amputation in adult monkeys," J. Comp. Neurol., vol. 224, pp. 591-605, 1984.
3. N. N. Pettet, "Dynamic changes in the receptive-field size in the primary visual cortex," Proc. Nat. Acad. Sci., vol. 89, pp. 8366-8370, 1992.
4. N. M. Weinberger, "Learning induced changes of auditory receptive fields," Curr. Opin. Neurobiol., vol. 3, pp. 570-577, 1993.
5. J. P. Donoghue, "Plasticity of adult sensorimotor representations," Curr. Opin. Neurobiol., vol. 5, no. 6, pp. 749-754, 1995.
6. M. S. Jog, Y. Kubota, C. I. Connolly, V. Hillegaart, and A. M. Graybiel, "Building neural representations of habits," Science, vol. 286, pp. 1745-1749, 1999.
7. J. Fritz, S. Shamma, M. Elhilali, and D. Klein, "Rapid task-related plasticity of spectrotemporal receptive fields in the primary auditory cortex," Nature Neurosci., vol. 6, no. 11, pp. 1216-1223, 2003.
8. C. S. R. Li, C. Padoa-Schioppa, and E. Bizzi, "Neuronal correlates of motor performance and motor learning in the primary motor cortex of monkeys adapting to an external force field," Neuron, vol. 30, no. 2, pp. 593-607, 2001.
9. M. R. Mehta, M. C. Quirk, and M. A. Wilson, "Experience dependent, asymmetric shape of hippocampal receptive fields," Neuron, vol. 25, no. 3, pp. 707-715, 2000.
10. L. M. Frank, U. T. Eden, V. Solo, M. A. Wilson, and E. N. Brown, "Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach," J. Neurosci., vol. 22, pp. 3817-3830, 2002.
11. L. M. Frank, G. B. Stanley, and E. N. Brown, "Hippocampal plasticity across multiple days of exposure to novel environments," J. Neurosci., vol. 24, no. 35, pp. 7681-7689, 2004.
12. J. K. Chapin, K. A. Moxon, R. S. Markowitz, and M. A. L. Nicolelis, "Real-time control of a robot arm using simultaneously recorded neurons in the motor cortex," Nature Neurosci., vol. 7, pp. 664-670, 1999.
13. J. Wessberg, C. R. Stambaugh, J. D. Kralik, P. D. Beck, M. Laubach, J. K. Chapin, J. Kim, S. J. Biggs, M. A. Srinivasan, and M. A. Nicolelis, "Real-time prediction of hand trajectory by ensembles of cortical neurons in primates," Nature, vol. 408, pp. 361-365, 2000.
14. M. D. Serruya, N. G. Hatsopoulos, L. Paninski, M. R. Fellows, and J. P. Donoghue, "Brain-machine interface: instant neural control of a movement signal," Nature, vol. 416, pp. 141-142, 2002.
15. D. M. Taylor, S. I. H. Tillery, and A. B. Schwartz, "Direct cortical control of 3D neuroprosthetic devices," Science, vol. 296, no. 5574, pp. 1829-1832, 2002.
16. C. Kemere, K. V. Shenoy, and T. H. Meng, "Model-based neural decoding of reaching movements: a maximum likelihood approach," IEEE Trans Biomed Eng., vol. 51, no. 6, pp. 925-932, Jun. 2004.
17. S. Mussallam, B. D. Corneil, B. Greger, H. Scherberger, and R. A. Andersen, "Cognitive control signals for neural prosthetics," Science, vol. 305, no. 5681, pp. 258-262, 2004.
18. W. Wu, Y. Gao, E. Bienenstock, J. P. Donoghue, and M. J. Black, "Bayesian population decoding of motor cortical activity using a kalman filter," Neural Comput., vol. 18, no. 1, pp. 80-118, 2006.
19. A. E. Brockwell, A. L. Rojas, and R. E. Kass, "Recursive Bayesian decoding of motor cortical signals by particle filtering," J. Neurophys., vol. 91, no. 2, pp. 1899-1907, 2004.
20. M. A. Wilson and B. L. McNaughton, "Dynamics of the hippocampal ensemble code for space," Science, vol. 261, pp. 1055-1058, 1993.
21. E. N. Brown, L. M. Frank, D. Tang, M. C. Quirk, and M. A. Wilson, "A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells," J. Neurosci., vol. 18, pp. 7411-7425, 1998.
22. R. Barbieri, L. M. Frank, D. P. Nguyen, M. C. Quirk, V. Solo, M. A. Wilson, and E. N. Brown, "Dynamic analyses of information encoding by neural ensembles," Neural Comp., vol. 16, no. 2, pp. 277-308, 2004.
23. U. T. Eden, L. M. Frank, R. Barbieri, V. Solo, and E. N. Brown, "Dynamic analyses of neural encoding by point process adaptive filtering," Neural Comp., vol. 16, no. 5, pp. 971-998, 2004.
24. E. N. Brown, D. P. Nguyen, L. M. Frank, M. A. Wilson, and V. Solo, "An analysis of neural receptive field plasticity by point process adaptive filtering," Proc. Nat. Acad. Sci., vol. 98, pp. 12261-12266, 2001.
25. J. M. Mendel, Lessons in Estimation Theory for Signal Processing, Communications and Control. Englewood Cliffs, NJ: Prentice-Hall, 1995.
26. G. Kitagawa and W. Gersh, Smoothness Priors Analysis of Time Series. New York: Springer, 1996.
27. M. Isard and A. Blake, "Visual tracking by stochastic propagation of conditional density," in Proc 4th Eur. Conf. Comp. Vision, 1996, pp. 343-356.
28. H. Wang and P. Chu, "Voice source localization for automatic camera pointing system in video conferencing," in Proc. ICASSP, 1997.
29. Y. Rui and Y. Chen, "Better proposal distributions: object tracking using unscented particle filter," in Proc. CVRP, 2001.
30. R. Van Der Merwe, A. Doucet, N. de Freitas, and E. Wan, The Unscented Particle Filter Cambridge Univ. Eng. Dept., , Tech. Rep. CUED/F-INFENG/TR 380, 2000.
31. A. Doucet, N. de Freitas, and N. Gordon, Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
32. _, "An introduction to sequential Monte Carlo methods," in Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
33. J. S. Liu and R. Chen, "Sequential Monte Carlo methods for dynamic systems," J. Am. Statist. Assoc., vol. 93, no. 443, pp. 1032-1044, 1998.
34. S. Shoham, "Advances Towards an Implantable Motor Cortical Interface," Unpublished Ph.D. dissertation, Univ. Utah, Salt Lake City, 2001.
35. D. Daley and D. Vere-Jones, An Introduction to the Theory of Point Process, 2nd ed. New-York: Springer-Verlag, 2003.
36. E. N. Brown, R. Barbieri, U. T. Eden, and L. M. Frank, "Likelihood methods for neural data analysis," in Computational Neuroscience: A Comprehensive Approach, J. Feng, Ed. London: CRC, 2003, pp. 253-286.
37. P. Djurić, "Sequential Estimation of Signals under Model Uncertainty," in Sequential Monte Carlo Methods in Practice. New York: Springer-Verlag, 2001.
38. S. Haykin, Adaptive Filter Theory, 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 1996.
39. M. A. Landolfa and G. A. Jacobs, "Direction sensitivity of the filiform hair population of the cricket cercal system," J. Comp. Physiol., vol. 177, pp. 759-766, 1995.
40. M. A. Landolfa and J. P. Miller, "Stimulus-response properties of cricket cercal filiform receptors," J. Comp Physiol., vol. 177, pp. 749-757, 1995.
41. F. Reike, D. Warland, R. d. R. van Steveninck, and W. Bialek, Spikes-Exploring the Neural Code. Cambridge, MA: The MIT Press, 1996.
42. E. N. Brown, R. Barbieri, V. Ventura, R. E. Kass, and L. M. Frank, "The time-rescaling theorem and its application to neural spike train data analysis," Neural Comp., vol. 14, no. 2, pp. 325-346, 2002.
43. A. C. Smith and E. N. Brown, "Estimating a state-space model from point process observations," Neural Comp., vol. 15, pp. 965-991, 2003.
44. S. J. Godsill, A. Doucet, and M. West, "Monte Carlo smoothing for nonlinear time series," JASA, vol. 99, no. 465, pp. 156-168, 2004, (13).
45. E. S. Chornoboy, P. L. Schramm, and A. F. Karr, "Maximum likelihood identification of neuronal point process systems," Biol. Cybern., vol. 59, pp. 265-275, 1988.
46. M. Okatan, M. A. Wilson, and E. N. Brown, "Analyzing functional connectivity using a network likelihood model of ensemble neural spiking activity," Neural Comp., vol. 9, pp. 1927-1961, 2005.
47. U. T. Eden, L. M. Frank, and E. N. Brown, "Particle filtering algorithms for neural decoding and adaptive estimation of receptive field plasticity," in Proc. Annu. Computational Neuroscience Meeting, Chicago, IL, Jul. 21-25, 2002.
48. F. P. Schoenber, "Multidimensional residual analysis of point process models for earthquake occurrences," JASA, vol. 98, no. 464, pp. 789-795, 2003, (7).
49. R. Barbieri, E. C. Matten, A. Alabi, and E. N. Brown, "A point process model of human heart beat intervals; new definitions of heart rate and heart variability," Am. J. Phys.: Heart Circ. Phys., vol. 288, pp. H424-H435, 2005.
50. P. F. Velleman and D. C. Hoaglin, Applications, Basics, and Computing of Exploratory Data Analysis. Boston, MA: Duxbury, 1981.