Bits from brains for biologically inspired computing

Frontiers Robotics AI

Volumn 2, Issue MAR, 2015, Pages

  Wibral, Michael ^a   Lizier, Joseph T ^b,c   Priesemann, Viola ^d,e  

^a GOETHE UNIVERSITY   (Germany)

^b UNIVERSITY OF SYDNEY   (Australia)

^c CSIRO   (Australia)

^d MAX PLANCK INSTITUTE FOR DYNAMICS AND SELF ORGANIZATION   (Germany)

^e BERNSTEIN CENTER FOR COMPUTATIONAL NEUROSCIENCE   (Germany)

Author keywords

Artificial neural networks; Biologically inspired computing; Computational intelligence; Information theory; Local information dynamics; Neural systems; Partial information decomposition

Indexed keywords

EID: 84953322975     PISSN: None     EISSN: 22969144     Source Type: Journal    
DOI: 10.3389/frobt.2015.00005     Document Type: Review

References (167)

1. Amblard, P. O., and Michel, O. J. (2011). On directed information theory and Granger causality graphs. J. Comput. Neurosci. 30, 7-16. doi: 10.1007/s10827-010-0231-x
2. Ay, N., and Polani, D. (2008). Information flows in causal networks. Adv. Complex Syst. 11, 17. doi:10.1142/S0219525908001465
3. Barnett, L., Barrett, A. B., and Seth, A. K. (2009). Granger causality and transfer entropy are equivalent for Gaussian variables. Phys. Rev. Lett. 103, 238701. doi:10.1103/PhysRevLett.103.238701
4. Barnett, L., Lizier, J. T., Harré, M., Seth, A. K., and Bossomaier, T. (2013). Information flow in a kinetic Ising model peaks in the disordered phase. Phys. Rev. Lett. 111, 177203. doi:10.1103/PhysRevLett.111.177203
5. Barrett, A. B. (2014). An exploration of synergistic and redundant information sharing in static and dynamical Gaussian systems. arXiv:1411.2832
6. Battaglia, D. (2014a). "Function follows dynamics: state-dependency of directed functional influences," in Directed Information Measures in Neuroscience eds M. Wibral, R. Vicente, and J. T. Lizier (Heidelberg: Springer), 111-135. doi:10.1007/978-3-642-54474-3
7. Battaglia, D. (2014b). "Function follows dynamics: state-dependency of directed functional influences," in Directed Information Measures in Neuroscience (Understanding Complex Systems), eds M. Wibral, R. Vicente, and J. T. Lizier (Berlin: Springer), 111-135
8. Battaglia, D., Witt, A., Wolf, F., and Geisel, T. (2012). Dynamic effective connectivity of inter-areal brain circuits. PLoS Comput. Biol. 8:e1002438. doi:10.1371/journal.pcbi.1002438
9. Bedo, N., Ribary, U., and Ward, L. M. (2014). Fast dynamics of cortical functional and effective connectivity during word reading. PLoS ONE 9:e88940. doi:10.1371/journal.pone.0088940
10. Beggs, J. M., and Plenz, D. (2003). Neuronal avalanches in neocortical circuits. J. Neurosci. 23, 11167-11177
11. Bertschinger, N., Rauh, J., Olbrich, E., and Jost, J. (2013). "Shared information-new insights and problems in decomposing information in complex systems," in Proceedings of the European Conference on Complex Systems 2012, eds T. Gilbert, M. Kirkilionis, and G. Nicolis (Springer), 251-269. doi:10.1007/978-3-319-00395-5_35
12. Bertschinger, N., Rauh, J., Olbrich, E., Jost, J., and Ay, N. (2014). Quantifying unique information. Entropy 16, 2161-2183. doi:10.3390/e16042161
13. Besserve, M., Scholkopf, B., Logothetis, N. K., and Panzeri, S. (2010a). Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. J. Comput. Neurosci. 29, 547-566. doi:10.1007/s10827-010-0236-5
14. Besserve, M., Schölkopf, B., Logothetis, N. K., and Panzeri, S. (2010b). Causal relationships between frequency bands of extracellular signals in visual cortex revealed by an information theoretic analysis. J. Comput. Neurosci. 29, 547-566. doi:10.1007/s10827-010-0236-5
15. Bettencourt, L. M., Gintautas, V., and Ham, M. I. (2008). Identification of functional information subgraphs in complex networks. Phys. Rev. Lett. 100, 238701. doi:10.1103/PhysRevLett.100.238701
16. Bialek, W., Nemenman, I., and Tishby, N. (2001). Predictability, complexity, and learning. Neural Comput. 13, 2409-2463. doi:10.1162/089976601753195969
17. Bikhchandani, S., Hirshleifer, D., and Welch, I. (1992). A theory of fads, fashions, custom, and cultural change as informational cascades. J. Polit. Econ. 100, 992-1026. doi:10.1086/261849
18. Boedecker, J., Obst, O., Lizier, J. T., Mayer, N. M., and Asada, M. (2012). Information processing in echo state networks at the edge of chaos. Theory Biosci. 131, 205-213. doi:10.1007/s12064-011-0146-8
19. Boedecker, J., Obst, O., Mayer, N. M., and Asada, M. (2009). Initialization and self-organized optimization of recurrent neural network connectivity. HFSP J. 3, 340-349. doi:10.2976/1.3240502
20. Buehlmann, A., and Deco, G. (2010). Optimal information transfer in the cortex through synchronization. PLoS Comput. Biol. 6:e1000934. doi:10.1371/journal.pcbi.1000934
21. Butail, S., Ladu, F., Spinello, D., and Porfiri, M. (2014). Information flow in animal-robot interactions. Entropy 16, 1315-1330. doi:10.3390/e16031315
22. Butts, D. A. (2003). How much information is associated with a particular stimulus? Network 14, 177-187. doi:10.1088/0954-898X/14/2/301
23. Butts, D. A., and Goldman, M. S. (2006). Tuning curves, neuronal variability, and sensory coding. PLoS Biol. 4:e92. doi:10.1371/journal.pbio.0040092
24. Carandini, M. (2012). From circuits to behavior: a bridge too far? Nat. Neurosci. 15, 507-509. doi:10.1038/nn.3043
25. Ceguerra, R. V., Lizier, J. T., and Zomaya, A. Y. (2011). "Information storage and transfer in the synchronization process in locally-connected networks," in 2011 IEEE Symposium on Artificial Life (ALIFE) (Paris: IEEE), 54-61
26. Chávez, M., Martinerie, J., and Le Van Quyen, M. (2003). Statistical assessment of nonlinear causality: application to epileptic EEG signals. J. Neurosci. Methods 124, 113-128. doi:10.1016/S0165-0270(02)00367-9
27. Chicharro, D. (2014). "Parametric and non-parametric criteria for causal inference from time-series," in Directed Information Measures in Neuroscience (Springer), 195-219
28. Chicharro, D., and Ledberg, A. (2012). When two become one: the limits of causality analysis of brain dynamics. PLoS ONE 7:e32466. doi:10.1371/journal.pone.0032466
29. Couzin, I. D., James, R., Croft, D. P., and Krause, J. (2006). "Social organization and information transfer in schooling fishes," in Fish Cognition and Behavior (Fish and Aquatic Resources), eds C. Brown, K. N. Laland, and J. Krause (New York: Blackwell Publishing), 166-185
30. Crowley, K., and Siegler, R. S. (1993). Flexible strategy use in young children's tic-tac-toe. Cogn. Sci. 17, 531-561. doi:10.1207/s15516709cog1704_3
31. Crutchfield, J. P., and Feldman, D. P. (2003). Regularities unseen, randomness observed: levels of entropy convergence. Chaos 13, 25-54. doi:10.1063/1.1530990
32. Crutchfield, J. P., and Packard, N. (1982). Symbolic dynamics of one-dimensional maps: entropies, finite precision, and noise. Int. J. Theor. Phys. 21, 433-466. doi:10.1007/BF02650178
33. Dasgupta, S., Wörgötter, F., and Manoonpong, P. (2013). Information dynamics based self-adaptive reservoir for delay temporal memory tasks. Evol. Syst. 4, 235-249. doi:10.1007/s12530-013-9080-y
34. Dewdney, A. (1989). A tinkertoy computer that plays tic-tac-toe. Scientific American 120-121
35. DeWeese, M. R., and Meister, M. (1999). How to measure the information gained from one symbol. Network 10, 325-340. doi:10.1088/0954-898X/10/4/303
36. Faes, L., Marinazzo, D., Montalto, A., and Nollo, G. (2014a). Lag-specific transfer entropy as a tool to assess cardiovascular and cardiorespiratory information transfer. IEEE Trans. Biomed. Eng. 61, 2556-2568. doi:10.1109/TBME.2014.2323131
37. Faes, L., Nollo, G., Jurysta, F., and Marinazzo, D. (2014b). Information dynamics of brain-heart physiological networks during sleep. New J. Phys. 16, 105005. doi:10.1088/1367-2630/16/10/105005
38. Faes, L., and Nollo, G. (2006). Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability. Med. Biol. Eng. Comput. 44, 383-392. doi:10.1007/s11517-006-0043-3
39. Faes, L., Nollo, G., and Porta, A. (2011a). Information-based detection of nonlinear granger causality in multivariate processes via a nonuniform embedding technique. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 83(5 Pt 1), 051112. doi:10.1103/PhysRevE.83.051112
40. Faes, L., Nollo, G., and Porta, A. (2011b). Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series. Comput. Biol. Med. 42, 290-297. doi:10.1016/j.compbiomed.2011.02.007
41. Faes, L., Nollo, G., and Porta, A. (2012). Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series. Comput. Biol. Med. 42, 290-297. doi:10.1016/j.compbiomed.2011.02.007
42. Faes, L., and Porta, A. (2014). "Conditional entropy-based evaluation of information dynamics in physiological systems," in Directed Information Measures in Neuroscience (Springer), 61-86
43. Fano, R. (1961). Transmission of Information. Cambridge, MA: The MIT Press
44. Fox, M. (2014). Chester nez, 93, dies; navajo words washed from mouth helped win war. The New York Times
45. Friston, K., Kilner, J., and Harrison, L. (2006). A free energy principle for the brain. J. Physiol. Paris 100, 70-87. doi:10.1016/j.jphysparis.2006.10.001
46. Gardner, W. A. (1994). "An introduction to cyclostationary signals," in Cyclostationarity in Communications and Signal Processing, ed. W. A. Garnder (New York: IEEE Press), 1-90
47. Gardner, W. A., Napolitano, A., and Paura, L. (2006). Cyclostationarity: half a century of research. Signal Processing 86, 639-697. doi:10.1016/j.sigpro.2005.06.016
48. Garofalo, M., Nieus, T., Massobrio, P., and Martinoia, S. (2009). Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks. PLoS ONE 4:e6482. doi:10.1371/journal.pone.0006482
49. Gomez, C., Lizier, J. T., Schaum, M., Wollstadt, P., Grützner, C., Uhlhaas, P., et al. (2014). Reduced predictable information in brain signals in autism spectrum disorder. Front. Neuroinformatics 8:9. doi:10.3389/fninf.2014.00009
50. Gomez-Herrero, G., Wu, W., Rutanen, K., Soriano, M. C., Pipa, G., and Vicente, R. (2010). Assessing coupling dynamics from an ensemble of time series. arXiv:1008.0539
51. Gourevitch, B., and Eggermont, J. J. (2007). Evaluating information transfer between auditory cortical neurons. J. Neurophysiol. 97, 2533-2543. doi:10.1152/jn.01106.2006
52. Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37, 424-438. doi:10.2307/1912791
53. Granot-Atedgi, E., Tkacik, G., Segev, R., and Schneidman, E. (2013). Stimulus-dependent maximum entropy models of neural population codes. PLoS Comput. Biol. 9:e1002922. doi:10.1371/journal.pcbi.1002922
54. Grassberger, P. (1986). Toward a quantitative theory of self-generated complexity. Int. J. Theor. Phys. 25, 907-938. doi:10.1007/BF00668821
55. Griffith, V., Chong, E. K. P., James, R. G., Ellison, C. J., and Crutchfield, J. P. (2014). Intersection information based on common randomness. Entropy 16, 1985-2000. doi:10.3390/e16041985
56. Griffith, V., and Koch, C. (2014). "Quantifying synergistic mutual information," in Guided Self-Organization: Inception, Volume 9 of Emergence, Complexity and Computation, ed. M. Prokopenko (Berlin: Springer), 159-190
57. Hadjipapas, A., Hillebrand, A., Holliday, I. E., Singh, K. D., and Barnes, G. R. (2005). Assessing interactions of linear and nonlinear neuronal sources using meg beamformers: a proof of concept. Clin. Neurophysiol. 116, 1300-1313. doi:10.1016/j.clinph.2005.01.014
58. Haldeman, C., and Beggs, J. M. (2005). Critical branching captures activity in living neural networks and maximizes the number of metastable states. Phys. Rev. Lett. 94, 058101. doi:10.1103/PhysRevLett.94.058101
59. Harder, M., Salge, C., and Polani, D. (2013). Bivariate measure of redundant information. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 87, 012130. doi:10.1103/PhysRevE.87.012130
60. Havenith, M. N., Yu, S., Biederlack, J., Chen, N.-H., Singer, W., and Nikolic, D. (2011). Synchrony makes neurons fire in sequence, and stimulus properties determine who is ahead. J. Neurosci. 31, 8570-8584. doi:10.1523/JNEUROSCI.2817-10.2011
61. Ito, S., Hansen, M. E., Heiland, R., Lumsdaine, A., Litke, A. M., and Beggs, J. M. (2011). Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model. PLoS ONE 6:e27431. doi:10.1371/journal.pone.0027431
62. Jaeger, H., and Haas, H. (2004). Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78-80. doi:10.1126/science.1091277
63. James, R. G., Ellison, C. J., and Crutchfield, J. P. (2011). Anatomy of a bit: information in a time series observation. Chaos 21, 037109. doi:10.1063/1.3637494
64. Johansson, R. S., and Birznieks, I. (2004). First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat. Neurosci. 7, 170-177. doi:10.1038/nn1177
65. Kawasaki, M., Uno, Y., Mori, J., Kobata, K., and Kitajo, K. (2014). Transcranial magnetic stimulation-induced global propagation of transient phase resetting associated with directional information flow. Front. Hum. Neurosci. 8:173. doi:10.3389/fnhum.2014.00173
66. Knill, D. C., and Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation. Trends Neurosci. 27, 712-719. doi:10.1016/j.tins.2004.10.007
67. Kozachenko, L., and Leonenko, N. (1987). Sample estimate of entropy of a random vector. Probl. Info. Transm. 23, 95-100
68. Kraskov, A., Stoegbauer, H., and Grassberger, P. (2004). Estimating mutual information. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 69(6 Pt 2), 066138. doi:10.1103/PhysRevE.69.066138
69. Kriegeskorte, N., Mur, M., Ruff, D. A., Kiani, R., Bodurka, J., Esteky, H., et al. (2008). Matching categorical object representations in inferior temporal cortex of man and monkey. Neuron 60, 1126-1141. doi:10.1016/j.neuron.2008.10.043
70. Landauer, R. (1961). Irreversibility and heat generation in the computing process. IBM J. Res. Dev. 5, 183-191. doi:10.1073/pnas.1219672110
71. Langton, C. G. (1990). Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42, 12-37. doi:10.1016/0167-2789(90)90064-V
72. Leistritz, L., Hesse, W., Arnold, M., and Witte, H. (2006). Development of interaction measures based on adaptive non-linear time series analysis of biomedical signals. Biomed. Tech. (Berl) 51, 64-69. doi:10.1515/BMT.2006.012
73. Li, X., and Ouyang, G. (2010). Estimating coupling direction between neuronal populations with permutation conditional mutual information. Neuroimage 52, 497-507. doi:10.1016/j.neuroimage.2010.05.003
74. Lindner, M., Vicente, R., Priesemann, V., and Wibral, M. (2011). Trentool: a Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci. 12:119. doi:10.1186/1471-2202-12-119
75. Lissaman, P. B. S., and Shollenberger, C. A. (1970). Formation flight of birds. Science 168, 1003-1005. doi:10.1126/science.168.3934.1003
76. Liu, T., and Pelowski, M. (2014). A new research trend in social neuroscience: towards an interactive-brain neuroscience. Psych J. 3, 177-188. doi:10.1002/pchj.56
77. Lizier, J. (2014a). "Measuring the dynamics of information processing on a local scale in time and space," in Directed Information Measures in Neuroscience, eds M. Wibral, R. Vicente, and J. T. Lizier (Berlin: Springer), 161-193. Understanding Complex Systems
78. Lizier, J. T. (2012c). JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems. Available at: http://code.google.com/p/information-dynamics-toolkit/
79. Lizier, J. T. (2013). The Local Information Dynamics of Distributed Computation in Complex Systems. Springer Theses, Springer, Berlin
80. Lizier, J. T. (2014b). JIDT: an information-theoretic toolkit for studying the dynamics of complex systems. Front. Robot. AI 1:11. doi:10.3389/frobt.2014.00011
81. Lizier, J. T., Flecker, B., and Williams, P. L. (2013). "Towards a synergy-based approach to measuring information modification," in 2013 IEEE Symposium on Artificial Life (ALIFE) (Singapore: IEEE), 43-51
82. Lizier, J. T., Heinzle, J., Horstmann, A., Haynes, J.-D., and Prokopenko, M. (2011a). Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity. J. Comput. Neurosci. 30, 85-107. doi:10.1007/s10827-010-0271-2
83. Lizier, J. T., Pritam, S., and Prokopenko, M. (2011b). Information dynamics in small-world Boolean networks. Artif. Life 17, 293-314. doi:10.1162/artl_a_00040
84. Lizier, J. T., and Prokopenko, M. (2010). Differentiating information transfer and causal effect. Eur. Phys. J. B 73, 605-615. doi:10.1140/epjb/e2010-00034-5
85. Lizier, J. T., Prokopenko, M., Tanev, I., and Zomaya, A. Y. (2008a). "Emergence of glider-like structures in a modular robotic system," in ALIFE, eds S. Bullock, J. Noble, R. Watson, and M. A. Bedau (Cambridge, MA: MIT Press), 366-373
86. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2008b). "The information dynamics of phase transitions in random Boolean networks," in Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (ALife XI), Winchester, UK, eds S. Bullock, J. Noble, R. Watson, and M. A. Bedau (Cambridge, MA: MIT Press), 374-381
87. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2008c). Local information transfer as a spatiotemporal filter for complex systems. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 77(2 Pt 2), 026110. doi:10.1103/PhysRevE.77.026110
88. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2007). "Detecting non-trivial computation in complex dynamics," in Proceedings of the 9th European Conference on Artificial Life (ECAL 2007), Volume 4648 of Lecture Notes in Computer Science, eds L. Almeida, M. Rocha, E. Costa, I. Harvey, and A. Coutinho (Berlin: Springer), 895-904
89. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2010). Information modification and particle collisions in distributed computation. Chaos 20, 037109. doi:10.1063/1.3486801
90. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2012a). Coherent information structure in complex computation. Theory Biosci. 131, 193-203. doi:10.1007/s12064-011-0145-9
91. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2012b). Local measures of information storage in complex distributed computation. Inf. Sci. 208, 39-54. doi:10.1016/j.ins.2012.04.016
92. Lizier, J. T., Prokopenko, M., and Zomaya, A. Y. (2014). "A framework for the local information dynamics of distributed computation in complex systems," in Guided Self-Organization: Inception, Volume 9 of Emergence, Complexity and Computation, ed. M. Prokopenko (Berlin: Springer), 115-158
93. Lizier, J. T., and Rubinov, M. (2012). Multivariate construction of effective computational networks from observational data. Max Planck Preprint 25/2012, Max Planck Institute for Mathematics in the Sciences
94. Lüdtke, N., Logothetis, N. K., and Panzeri, S. (2010). Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling. Magn. Reson. Imaging 28, 1113-1119. doi:10.1016/j.mri.2010.03.028
95. Maass, W., Natschläger, T., and Markram, H. (2002). Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531-2560. doi:10.1162/089976602760407955
96. MacKay, D. J. C. (2003). Information Theory, Inference and Learning Algorithms. Cambridge, MA: Cambridge University Press
97. Marinazzo, D., Gosseries, O., Boly, M., Ledoux, D., Rosanova, M., Massimini, M., et al. (2014a). Directed information transfer in scalp electroencephalographic recordings: insights on disorders of consciousness. Clin. EEG Neurosci. 45, 33-39. doi:10.1177/1550059413510703
98. Marinazzo, D., Pellicoro, M., Wu, G., Angelini, L., Cortés, J. M., and Stramaglia, S. (2014b). Information transfer and criticality in the Ising model on the human connectome. PLoS ONE 9:e93616. doi:10.1371/journal.pone.0093616
99. Marinazzo, D., Wu, G., Pellicoro, M., and Stramaglia, S. (2014c). "Information transfer in the brain: insights from a unified approach," in Directed Information Measures in Neuroscience (Understanding Complex Systems), eds M. Wibral, R. Vicente, and J. T. Lizier (Berlin: Springer), 87-110
100. Markram, H. (2006). The blue brain project. Nat. Rev. Neurosci. 7, 153-160. doi:10.1038/nrn1848
101. Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. New York, NY: Henry Holt and Co., Inc
102. McAuliffe, J. (2014). 14. The new math of EEG: symbolic transfer entropy, the effects of dimension. Neurophysiol. Clin. 125, e17. doi:10.1016/j.clinph.2013.12.017
103. Mitchell, M. (1998). "Computation in cellular automata: a selected review," in Non-Standard Computation, eds T. Gramß, S. Bornholdt, M. Groß, M. Mitchell, and T. Pellizzari (Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA), 95-140
104. Montalto, A., Faes, L., and Marinazzo, D. (2014). Mute: a matlab toolbox to compare established and novel estimators of the multivariate transfer entropy. PLoS ONE 9:e109462. doi:10.1371/journal.pone.0109462
105. Neymotin, S. A., Jacobs, K. M., Fenton, A. A., and Lytton, W. W. (2011). Synaptic information transfer in computer models of neocortical columns. J. Comput. Neurosci. 30, 69-84. doi:10.1007/s10827-010-0253-4
106. O'Keefe, J., and Recce, M. L. (1993). Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317-330. doi:10.1002/hipo.450030307
107. Orlandi, J. G., Stetter, O., Soriano, J., Geisel, T., and Battaglia, D. (2014). Transfer entropy reconstruction and labeling of neuronal connections from simulated calcium imaging. PLoS ONE 9:e98842. doi:10.1371/journal.pone.0098842
108. Paluš, M. (2001). Synchronization as adjustment of information rates: detection from bivariate time series. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 63, 046211. doi:10.1103/PhysRevE.63.046211
109. Panzeri, S., Senatore, R., Montemurro, M. A., and Petersen, R. S. (2007a). Correcting for the sampling bias problem in spike train information measures. J. Neurophysiol. 98, 1064-1072. doi:10.1152/jn.00559.2007
110. Panzeri, S., Senatore, R., Montemurro, M. A., and Petersen, R. S. (2007b). Correcting for the sampling bias problem in spike train information measures. J. Neurophysiol. 98, 1064-1072. doi:10.1152/jn.00559.2007
111. Parrish, J. K., and Edelstein-Keshet, L. (1999). Complexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284, 99-101. doi:10.1126/science.284.5411.99
112. Porta, A., Faes, L., Bari, V., Marchi, A., Bassani, T., Nollo, G., et al. (2014). Effect of age on complexity and causality of the cardiovascular control: comparison between model-based and model-free approaches. PLoS ONE 9:e89463. doi:10.1371/journal.pone.0089463
113. Porta, A., Guzzetti, S., Montano, N., Pagani, M., Somers, V., Malliani, A., et al. (2000). Information domain analysis of cardiovascular variability signals: evaluation of regularity, synchronisation and co-ordination. Med. Biol. Eng. Comput. 38, 180-188. doi:10.1007/BF02344774
114. Priesemann, V., Munk, M. H. J., and Wibral, M. (2009). Subsampling effects in neuronal avalanche distributions recorded in vivo. BMC Neurosci. 10:40. doi:10.1186/1471-2202-10-40
115. Priesemann, V., Valderrama, M., Wibral, M., and Le Van Quyen, M. (2013a). Neuronal avalanches differ from wakefulness to deep sleep-evidence from intracranial depth recordings in humans. PLoS Comput. Biol. 9:e1002985. doi:10.1371/journal.pcbi.1002985
116. Priesemann, V., Wibral, M., and Triesch, J. (2013b). Learning more by sampling less: subsampling effects are model specific. BMC Neurosci. 14(Suppl. 1):414. doi:10.1186/1471-2202-14-S1-P414
117. Priesemann, V., Wibral, M., Valderrama, M., Pröpper, R., Le Van Quyen, M., Geisel, T., et al. (2014). Spike avalanches in vivo suggest a driven, slightly subcritical brain state. Front. Syst. Neurosci. 8:00001. doi:10.3389/fnsys.2014.00108
118. Prokopenko, M. (2009). Guided self-organization. HFSP J. 3, 287-289. doi:10.2976/1.3233933
119. Prokopenko, M. (ed.) (2014). Guided Self-Organization: Inception, Volume 9 of Emergence, Complexity and Computation. Berlin: Springer
120. Prokopenko, M., and Lizier, J. T. (2014). Transfer entropy and transient limits of computation. Sci. Rep. 4, 5394. doi:10.1038/srep05394
121. Ragwitz, M., and Kantz, H. (2002). Markov models from data by simple nonlinear time series predictors in delay embedding spaces. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 65(5 Pt 2), 056201. doi:10.1103/PhysRevE.65.056201
122. Rao, R. P., and Ballard, D. H. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nat. Neurosci. 2, 79-87. doi:10.1038/4580
123. Rauh, J., Bertschinger, N., Olbrich, E., and Jost, J. (2014). Reconsidering unique information: towards a multivariate information decomposition. arXiv:1404.3146
124. Razak, F. A., and Jensen, H. J. (2014). Quantifying 'causality' in complex systems: understanding transfer entropy. PLoS ONE 9:e99462. doi:10.1371/journal.pone.0099462
125. Reynolds, C. W. (1987). "Flocks, herds and schools: a distributed behavioral model," in SIGGRAPH '87 Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, Volume 21, ACM (New York, NY: ACM), 25-34
126. Rowan, M. S., Neymotin, S. A., and Lytton, W. W. (2014). Electrostimulation to reduce synaptic scaling driven progression of Alzheimer's disease. Front. Comput. Neurosci. 8:39. doi:10.3389/fncom.2014.00039
127. Sabesan, S., Good, L. B., Tsakalis, K. S., Spanias, A., Treiman, D. M., and Iasemidis, L. D. (2009). Information flow and application to epileptogenic focus localization from intracranial EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 17, 244-253. doi:10.1109/TNSRE.2009.2023291
128. Schrauwen, B., Wardermann, M., Verstraeten, D., Steil, J. J., and Stroobandt, D. (2008). Improving reservoirs using intrinsic plasticity. Neurocomputing 71, 1159-1171. doi:10.1016/j.neucom.2007.12.020
129. Schreiber, T. (2000). Measuring information transfer. Phys. Rev. Lett. 85, 461-464. doi:10.1103/PhysRevLett.85.461
130. Shalizi, C. R. (2001). Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. Ph.D. thesis, University of Wisconsin-Madison, Madison, WI
131. Shannon, C. E. (1948). A mathematical theory of communication. Bell Sys. Tech. J. 27, 379-423. doi:10.1002/j.1538-7305.1948.tb01338.x
132. Shew, W. L., and Plenz, D. (2013). The functional benefits of criticality in the cortex. Neuroscientist 19, 88-100. doi:10.1177/1073858412445487
133. Shimono, M., and Beggs, J. M. (2014). Functional clusters, hubs, and communities in the cortical microconnectome. Cereb. Cortex doi:10.1093/cercor/bhu252
134. Small, M., and Tse, C. (2004). Optimal embedding parameters: a modelling paradigm. Physica D 194, 283-296. doi:10.1016/j.physd.2004.03.006
135. Smirnov, D. A. (2013). Spurious causalities with transfer entropy. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 87, 042917. doi:10.1103/PhysRevE.87.042917
136. Staniek, M., and Lehnertz, K. (2009). Symbolic transfer entropy: inferring directionality in biosignals. Biomed. Tech. (Berl) 54, 323-328. doi:10.1515/BMT.2009.040
137. Stetter, O., Battaglia, D., Soriano, J., and Geisel, T. (2012). Model-free reconstruction of excitatory neuronal connectivity from calcium imaging signals. PLoS Comput. Biol. 8:e1002653. doi:10.1371/journal.pcbi.1002653
138. Stramaglia, S., Cortes, J. M., and Marinazzo, D. (2014). Synergy and redundancy in the granger causal analysis of dynamical networks. New J. Phys. 16, 105003. doi:10.1088/1367-2630/16/10/105003
139. Stramaglia, S., Wu, G.-R., Pellicoro, M., and Marinazzo, D. (2012). Expanding the transfer entropy to identify information circuits in complex systems. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 86, 066211. doi:10.1103/PhysRevE.86.066211
140. Szilárd, L. (1929). über die entropieverminderung in einem thermodynamischen system bei eingriffen intelligenter wesen (On the reduction of entropy in a thermodynamic system by the intervention of intelligent beings). Z. Phys. 53, 840-856. doi:10.1007/BF01341281
141. Takens, F. (1981). "Detecting strange attractors in turbulence, chapter 21," in Dynamical Systems and Turbulence, Warwick 1980, Volume 898 of Lecture Notes in Mathematics, eds D. Rand and L.-S. Young (Berlin: Springer), 366-381
142. Thivierge, J.-P. (2014). Scale-free and economical features of functional connectivity in neuronal networks. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 90, 022721. doi:10.1103/PhysRevE.90.022721
143. Timme, N., Alford, W., Flecker, B., and Beggs, J. M. (2014). Synergy, redundancy, and multivariate information measures: an experimentalist's perspective. J. Comput. Neurosci. 36, 119-140. doi:10.1007/s10827-013-0458-4
144. Tkacik, G., Prentice, J. S., Balasubramanian, V., and Schneidman, E. (2010). Optimal population coding by noisy spiking neurons. Proc. Natl. Acad. Sci. U.S.A. 107, 14419-14424. doi:10.1073/pnas.1004906107
145. Treves, A., and Panzeri, S. (1995). The upward bias in measures of information derived from limited data samples. Neural Comput. 7, 399-407. doi:10.1162/neco.1995.7.2.399
146. Untergehrer, G., Jordan, D., Kochs, E. F., Ilg, R., and Schneider, G. (2014). Fronto-parietal connectivity is a non-static phenomenon with characteristic changes during unconsciousness. PLoS ONE 9:e87498. doi:10.1371/journal.pone.0087498
147. Vakorin, V. A., Kovacevic, N., and McIntosh, A. R. (2010). Exploring transient transfer entropy based on a group-wise ICA decomposition of eeg data. Neuroimage 49, 1593-1600. doi:10.1016/j.neuroimage.2009.08.027
148. Vakorin, V. A., Krakovska, O. A., and McIntosh, A. R. (2009). Confounding effects of indirect connections on causality estimation. J. Neurosci. Methods 184, 152-160. doi:10.1016/j.jneumeth.2009.07.014
149. Vakorin, V. A., Mišic, B., Krakovska, O., and McIntosh, A. R. (2011). Empirical and theoretical aspects of generation and transfer of information in a neuromagnetic source network. Front. Syst. Neurosci. 5:96. doi:10.3389/fnsys.2011.00096
150. van Mierlo, P., Papadopoulou, M., Carrette, E., Boon, P., Vandenberghe, S., Vonck, K., et al. (2014). Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization. Prog. Neurobiol. 121, 19-35. doi:10.1016/j.pneurobio.2014.06.004
151. Varon, C., Montalto, A., Jansen, K., Lagae, L., Marinazzo, D., Faes, L., et al. (2014). "Interictal cardiorespiratory variability in temporal lobe and absence epilepsy in childhood," in Proc. of the 8th Conference of the European Study Group on Cardiovascular Osciliations. Tento
152. Vicente, R., Wibral, M., Lindner, M., and Pipa, G. (2011). Transfer entropy-a model-free measure of effective connectivity for the neurosciences. J. Comput. Neurosci. 30, 45-67. doi:10.1007/s10827-010-0262-3
153. Victor, J. (2005). Binless strategies for estimation of information from neural data. Phys. Rev. E Stat. Nonlin. Soft Matter Phys. 72, 051903. doi:10.1103/PhysRevE.66.051903
154. Wang, X. R., Lizier, J. T., and Prokopenko, M. (2011). Fisher information at the edge of chaos in random Boolean networks. Artif. Life 17, 315-329. doi:10.1162/artl_a_00041
155. Wang, X. R., Miller, J. M., Lizier, J. T., Prokopenko, M., and Rossi, L. F. (2012). Quantifying and tracing information cascades in swarms. PLoS ONE 7:e40084. doi:10.1371/journal.pone.0040084
156. Wibral, M., Lizier, J. T., Vögler, S., Priesemann, V., and Galuske, R. (2014a). Local active information storage as a tool to understand distributed neural information processing. Front. Neuroinformatics 8:1. doi:10.3389/fninf.2014.00001
157. Wibral, M., Vicente, R., and Lindner, M. (2014b). "Transfer entropy in neuroscience," in Directed Information Measures in Neuroscience (Understanding Complex Systems), eds M. Wibral, R. Vicente, and J. T. Lizier (Berlin: Springer), 3-36
158. Wibral, M., Pampu, N., Priesemann, V., Siebenhühner, F., Seiwert, H., Lindner, M., et al. (2013). Measuring information-transfer delays. PLoS ONE 8:e55809. doi:10.1371/journal.pone.0055809
159. Wibral, M., Rahm, B., Rieder, M., Lindner, M., Vicente, R., and Kaiser, J. (2011). Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks. Prog. Biophys. Mol. Biol. 105, 80-97. doi:10.1016/j.pbiomolbio.2010.11.006
160. Wibral, M., Turi, G., Linden, D. E. J., Kaiser, J., and Bledowski, C. (2008). Decomposition of working memory-related scalp ERPS: crossvalidation of fMRI-constrained source analysis and ICA. Int. J. Psychophysiol. 67, 200-211. doi:10.1016/j.ijpsycho.2007.06.009
161. Wiener, N. (1956). "The theory of prediction," in Modern Mathematics for the Engineer, ed. E. F. Beckmann (New York, NY: McGraw-Hill), p. 165-190
162. Williams, P. L., and Beer, R. D. (2010). Nonnegative decomposition of multivariate information. arXiv:1004.2515
163. Wolfram, S. (2002). A New Kind of Science. Champaign, IL: Wolfram Media
164. Wollstadt, P., Martínez-Zarzuela, M., Vicente, R., Díaz-Pernas, F. J., and Wibral, M. (2014). Efficient transfer entropy analysis of non-stationary neural time series. PLoS ONE 9:e102833. doi:10.1371/journal.pone.0102833
165. Yamaguti, Y., and Tsuda, I. (2014). Mathematical modeling for evolution of heterogeneous modules in the brain. Neural Netw. 62, 3-10. doi:10.1016/j.neunet.2014.07.013
166. Zipser, D., Kehoe, B., Littlewort, G., and Fuster, J. (1993). A spiking network model of short-term active memory. J. Neurosci. 13, 3406-3420
167. Zubler, F., Gast, H., Abela, E., Rummel, C., Hauf, M., Wiest, R., et al. (2014). Detecting functional hubs of ictogenic networks. Brain Topogr. 28(2), 305-17. doi:10.1007/s10548-014-0370-x