The Ising decoder: Reading out the activity of large neural ensembles

Journal of Computational Neuroscience

Volumn 32, Issue 1, 2012, Pages 101-118

  Schaub, Michael T ^a   Schultz, Simon R ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

BCI; Brain computer interface; Brain machine interface; Correlations; Decoding algorithm; Neural coding; Spatial patterns; Synchrony; Visual cortex

Indexed keywords

BRAIN; DECODING; INFERENCE ENGINES; INTERFACES (COMPUTER); ISING MODEL; NEURAL NETWORKS; NEURONS; STATISTICAL PHYSICS; STATISTICAL TESTS;

BCI; BRAIN MACHINE INTERFACE; CORRELATION; DECODING ALGORITHM; MACHINE INTERFACES; NEURAL CODING; NEURAL ENSEMBLES; SPATIAL PATTERNS; SYNCHRONY; VISUAL CORTEXES;

BRAIN COMPUTER INTERFACE;

ARTICLE; BRAIN COMPUTER INTERFACE; COMPUTER SIMULATION; CONTROLLED STUDY; DATA PROCESSING; INFERENTIAL STATISTICS; ISING DECODER; LEARNING ALGORITHM; MINIMUL PROBABILITY FLOW LEARNING ALGORITHM; NERVE CELL NETWORK; NERVE FUNCTION; NERVE POTENTIAL; ONLINE ANALYSIS; PREDICTION; PRIORITY JOURNAL; STATISTICAL ANALYSIS; STATISTICAL PARAMETERS; VISUAL CORTEX;

EID: 84858796180     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-011-0342-z     Document Type: Article

References (55)

1. Aghagolzadeh, M., Eldawlatly, S., & Oweiss, K. (2010). Synergistic coding by cortical neural ensembles. IEEE Transactions on Information Theory, 56(2), 875-899.
2. Baddeley, R., Abbott, L. F., Booth, M. C., Sengpiel, F., Freeman, T., Wakeman, E. A., et al. (1997). Responses of neurons in primary and inferior temporal visual cortices to natural scenes. Proceedings of the Royal Society B: Biological Sciences, 264(1389), 1775-1783. (Pubitemid 28021588)
3. Bair, W., & Koch, C. (1996). Temporal precision of spike trains in extrastriate cortex of the behaving macaque monkey. Neural Computation, 8(6), 1185-1202. (Pubitemid 126449965)
4. Bair, W., Zohary, E., & Newsome, W. T. (2001). Correlated firing in macaque visual area MT: Time scales and relationship to behavior. Journal of Neuroscience, 21(5), 1676-1697. (Pubitemid 32182208)
5. Berger, A. L., Pietra, V. J. D., & Pietra, S. A. D. (1996). A maximum entropy approach to natural language processing. Computational Linguistics, 22(1), 39-71.
6. Bi, G.-Q., & Poo, M.-M. (2001). Synaptic modification by correlated activity: Hebb's postulate revisited. Annual Review of Neuroscience, 24(1), 139-166. (Pubitemid 32695226)
7. Bishop, C. M. (2007). Pattern recognition and machine learning (Information science and statistics) (1st ed., 2006; corr. 2nd printing ed.). New York: Springer.
8. Broderick, T., Dudík, M., Tkačik, G., Schapire, R. E., & Bialek, W. (2007). Faster solutions of the inverse pairwise Ising problem. arXiv:0712.2437v2.
9. Butts, D. A., Weng, C., Jin, J., Yeh, C.-I. I., Lesica, N. A., Alonso, J.-M.M., et al. (2007). Temporal precision in the neural code and the timescales of natural vision. Nature, 449(7158), 92-95. (Pubitemid 47373774)
10. Carr, C. E. (1993). Processing of temporal information in the brain. Annual Review of Neuroscience, 16, 223-243.
11. Chen, Y., Geisler, W. S.,&Seidemann, E. (2006).Optimal decoding of correlated neural population responses in the primate visual cortex. Nature. Neuroscience, 9(11), 1412-1420. (Pubitemid 44646214)
12. Das, A., & Gilbert, C. D. (1999). Topography of contextual modulations mediated by short-range interactions in primary visual cortex. Nature, 399, 643-644.
13. Ecker, A. S., Berens, P., Keliris, G. A., Bethge, M., Logothetis, N. K., & Tolias, A. S. (2010). Decorrelated neuronal firing in cortical microcircuits. Science, 327(5965), 584-587.
14. Földiák, P. (1993). The 'ideal homunculus': Statistical inference from neural population responses. In Computation and neural systems (pp. 55-60). Norwell: Kluwer Academic.
15. Gawne, T. J., Kjaer, T. W., Hertz, J. A., & Richmond, B. J. (1996). Adjacent visual cortical complex cells share about 20% of their stimulus-related information. Cerebral Cortex, 6(3), 482-489.
16. Hertz, J., Roudi, Y., Thorning, A., Tyrcha, J., Aurell, E., & Zeng, H. L. (2010). Inferring network connectivity using kinetic Ising models. BMC Neuroscience, 11(Suppl 1), P51.
17. Higham, N. J. (2002). Computing the nearest correlation matrix - A problem from finance. IMA Journal of Numerical Analysis, 22(3), 329.
18. Huang, F., & Ogata, Y. (2001). Comparison of two methods for calculating the partition functions of various spatial statistical models. Australian & New Zealand Journal of Statistics, 43(1), 47-65. (Pubitemid 33613429)
19. Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review, 106(4), 620-630.
20. Kappen, H. J., & Rodríguez, F. B. (1998). Efficient learning in Boltzmann machines using linear response theory. Neural Computation, 10(5), 1137-1156. (Pubitemid 128463666)
21. Kohn, A., & Smith, M. A. (2005). Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. Journal of Neuroscience, 25(14), 3661-3673.
22. Kohn, A., Zandvakili, A., & Smith, M. A. (2009). Correlations and brain states: From electrophysiology to functional imaging. Current Opinion in Neurobiology, 19(4), 434-438.
23. Macke, J. H., Berens, P., Ecker, A. S., Tolias, A. S., & Bethge, M. (2009). Generating spike trains with specified correlation coefficients. Neural Computation, 21(2), 397-423.
24. Mak, J. N., & Wolpaw, J.R. (2009). Clinical applications of brain- computer interfaces: Current state and future prospects. Reviews in Biomedical Engineering, 2, 187-199.
25. Montani, F., Kohn, A., Smith,M. A., & Schultz, S. R. (2007). The role of correlations in direction and contrast coding in the primary visual cortex. Journal of Neuroscience, 27(9), 2338-2348. (Pubitemid 46340967)
26. Nase,G., Singer,W., Monyer,H.,&Engel,A. K. (2003). Features of neuronal synchrony in mouse visual cortex. Journal of Neurophysiology, 90(2), 1115-1123. (Pubitemid 37092591)
27. Niell, C. M., & Stryker, M. P. (2008). Highly selective receptive fields in mouse visual cortex. Journal of Neuroscience, 28(30), 7520-7536.
28. Ogata, Y., & Tanemura, M. (1984). Likelihood analysis of spatial point patterns. Journal of the Royal Statistical Society. Series B, 46(3), 496-518.
29. Ohiorhenuan, I. E., Mechler, F., Purpura, K. P., Schmid, A. M., Hu, Q., & Victor, J. D. (2010). Sparse coding and highorder correlations in fine-scale cortical networks. Nature, 466(7306), 617-621.
30. Oram, M. W., Földiíak, P., Perrett, D. I., Oram, M. W., & Sengpiel, F. (1998). The 'ideal homunculus': Decoding neural population signals. Trends in Neurosciences, 21(6), 259-265. (Pubitemid 28239984)
31. Panzeri, S., & Schultz, S. R. (2001). A unified approach to the study of temporal, correlational, and rate coding. Neural Computation, 13(6), 1311-1349. (Pubitemid 33595840)
32. Panzeri, S., Schultz, S. R., Treves, A., & Rolls, E. T. (1999a). Correlations and the encoding of information in the nervous system. Proceedings of the Royal Society of London. Series B: Biological Sciences, 266(1423), 1001-1012. (Pubitemid 29237998)
33. Panzeri, S., Treves, A., Schultz, S., & Rolls, E. T. (1999b). On decoding the responses of a population of neurons from short time windows. Neural Computation, 11(7), 1553-1577.
34. Petersen, R. S., Panzeri, S., & Diamond, M. E. (2001). Population coding of stimulus location in rat somatosensory cortex. Neuron, 32(3), 503-514. (Pubitemid 33085534)
35. Plefka, T. (2006). Expansion of the Gibbs potential for quantum many-body systems: General formalism with applications to the spin glass and the weakly nonideal Bose gas. Physical Review E, 73(1), 016129.
36. Pola,G., Thiele,A., Hoffmann, K.,&Panzeri, S. (2003).An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network-Computation in Neural Systems, 14(1), 35-60. (Pubitemid 38010857)
37. Reich, D. S., Mechler, F.,&Victor, J.D. (2001). Independent and redundant information in nearby cortical neurons. Science, 294(5551), 2566-2568. (Pubitemid 34027300)
38. Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., et al. (2010). The asynchronous state in cortical circuits. Science, 327(5965), 587-590.
39. Roudi, Y., Aurell, E., & Hertz, J. A. (2009a). Statistical physics of pairwise probability models. Frontiers in Computational Neuroscience, 3:22, 1-15.
40. Roudi, Y., & Hertz, J. (2011). Mean field theory for nonequilibrium network reconstruction. Physical Review Letters, 106, 048702.
41. Roudi, Y., Nirenberg, S., & Latham, P. E. (2009b). Pairwise maximum entropy models for studying large biological systems: When they can work and when they can't. PLoS Computational Biology, 5(5), e1000380.
42. Roudi, Y., Tyrcha, J., & Hertz, J. (2009c). Ising model for neural data:Model quality and approximate methods for extracting functional connectivity. Physical Review E, 79(5), 051915 (12 pages).
43. Samonds, J.M., & Bonds, A. B. (2005). Gamma oscillation maintains stimulus structure-dependent synchronization in cat visual cortex. Journal of Neurophysiology, 93(1), 223-236. (Pubitemid 40626836)
44. Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani,M., et al. (2009). Factor-analysis methods for higher-performance neural prostheses. Journal of Neurophysiology, 102(2), 1315-1330.
45. Schneidman, E., Berry II, M. J., Segev, R., & Bialek, W. (2006). Weak pairwise correlations imply strongly correlated network states in a neural population. Nature, 440(7087), 1007-1012.
46. Schultz, S. R., Kitamura, K., Post-Uiterweer, A., Krupic, J., & Hausser, M. (2009). Spatial pattern coding of sensory information by climbing fiber-evoked calcium signals in networks of neighboring cerebellar Purkinje cells. Journal of Neuroscience, 29(25), 8005-8015.
47. Schultz, S. R., & Panzeri, S. (2001). Temporal correlations and neural spike train entropy. Physical Review Letters, 86(25), 5823-5826. (Pubitemid 32577038)
48. Seiler, H., Zhang, Y., Saleem, A., Bream, P., Apergis-Schoute, J., & Schultz, S. R. (2009). Maximum entropy decoding of multivariate neural spike trains. BMC Neuroscience, 10(Suppl 1), P107.
49. Shlens, J., Field, G. D., Gauthier, J. L., Greschner, M., Sher, A., Litke, A. M., et al. (2009). The structure of large-scale synchronized firing in primate retina. Journal of Neuroscience, 29(15), 5022-5031.
50. Shlens, J., Field, G. D., Gauthier, J. L., Grivich, M. I., Petrusca, D., Sher, A., et al. (2006). The structure of multi-neuron firing patterns in primate retina. Journal of Neuroscience, 26(32), 8254-8266. (Pubitemid 44309839)
51. Sohl-Dickstein, J., Battaglino, P.,&DeWeese, M. R. (2009). Minimum probability f low learning. arXiv:0906.4779v2.
52. Tanaka, T. (1998). Mean-field theory of Boltzmann machine learning. Physical Review E, 58(2), 2302-2310.
53. Thouless, D. J., Anderson, P. W., & Palmer, R. G. (1977). Solution of solvable model of a spin glass. Philosophical Magazine, 35(3), 593-601.
54. Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. Clinical Neurophysiology, 113(6), 767-791. (Pubitemid 34621187)
55. Zohary, E., & Shadlen,M. (1994). Correlated neuronal discharge rate and its implications for psychophysical performance. Nature, 370(6485), 140-143. (Pubitemid 24229125)