Application of Lempel-Ziv complexity to the analysis of neural discharges

Network: Computation in Neural Systems

Volumn 14, Issue 2, 2003, Pages 335-350

  Szczepanski J ^a,b   Amigo J M ^c   Wajnryb, E ^a   Sanchez Vives, M V ^d  

^a INSTITUTE OF FUNDAMENTAL TECHNOLOGICAL RESEARCH   (Poland)

^b Centre of Trust and Certification   (Poland)

^c MIGUEL HERNÁNDEZ UNIVERSITY   (Spain)

^d INSTITUTO DE NEUROCIENCIAS   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ANIMAL; ANIMAL CELL; ANIMAL EXPERIMENT; ANIMAL TISSUE; ARTICLE; BIOLOGICAL MODEL; CAT; CONTROLLED STUDY; ELECTROSTIMULATION; ENTROPY; FEMALE; GRATING; IN VITRO STUDY; IN VIVO STUDY; INJECTION; INTRACELLULAR SPACE; MALE; MATHEMATICAL COMPUTING; NERVE CELL; NERVE TRACT; NONHUMAN; PHYSIOLOGY; SPIKE; STIMULUS; VISUAL CORTEX; VISUAL STIMULATION;

ACTION POTENTIALS; ANIMALS; CATS; ENTROPY; MODELS, NEUROLOGICAL; NEURAL PATHWAYS; NEURONS; PHYSIOLOGY;

EID: 17144432611     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_14_2_309     Document Type: Article

References (31)

1. Amigó JM, Szczepański J, Wajnryb E and Sanchez-Vives M V 2001 On the number of states of the sources as defined by neuron responses of primary visual cortex Proc. World Congr. on Neuroinformatics (ARGESIM Report No 20) ed F Ratta (Vienna: ARGESIM/ASIM) (ISBN: 3-901608-20-6)
2. Amigó J M, Szczepański J, Wajnryb E and Sanchez-Vives M V 2003 On the number of states of the neuronal sources Biosystems 68 57-66
3. Borst A and Theunissen FE 1999 Information theory and neural coding Nature Neurosci. 2 947-57
4. Chaitin G J 1982 Algorithmic information theory Encyclopedia of Statistical Sciences vol 1 (New York: Wiley)
5. Cover T M and Thomas J A 1991 Elements of Information Theory (New York: Wiley)
6. Dan Y, Atick J J and Reid R C 1996 Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory J. Neurosci. 16 3351-62
7. Ebeling W, Molgedey L, Kurths J and Schwarz U 2000 Entropy, complexity, predictability and data analysis of time series and letter sequences Preprint http://summa.physik.hu-berlin.de/tsd/publication.shtml
8. Farach M, Noordewier M, Savari S, Shepp L, Wyner A J and Ziv J 1995 On the entropy of DNA: algorithms and measurements based on memory and rapid convergence Proc. 6th Ann. ACM-SIAM Symp. on Discrete Algorithms (Philadelphia, PA: SIAM) pp 48-57
9. Gallanger R C 1968 Information Theory and Reliable Communications (New York: Wiley)
10. Gonzalez Andino S L, Grave de Peralta Menendez R, Thut G, Spinelli L, Blanke O, Michel C M, Seeck M and Landis T 2000 Measuring the complexity of times series: an application to neurophysiological signals Human Brain Mapp. 11 46-57
11. Hodgkin A L and Huxley A F 1952 Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo J. Physiol. 116 449-72
12. Holt G R, Softky W R, Koch C and Douglas R J 1996 Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons J. Neurophys. 75 1806-14
13. Kontoyiannis I, Algoet P H, Suhov Y M and Wyner A J 1998 Nonparametric entropy estimation for stationary processes and random fields, with applications to English text IEEE Trans. Inf. Theory 44 1319-27
14. Lempel A and Ziv J 1976 On the complexity of an individual sequence IEEE Trans. Inf. Theory 22 75-88
15. Leung A K and Tavares S E 1985 Sequence complexity as a test for cryptographic systems Proc. CRYPTO'84 (LNCS 196) ed G R Blackley and D Chaum (Berlin: Springer) pp 468-74
16. MacKay D and McCulloch W S 1952 The limiting information capacity of a neuronal link Bull. Math. Biophys. 14 127-35
17. McFadden J A 1995 The entropy of a point process J. SIAM 13 988
18. Rapp P E, Cellucci C J, Korslund K E, Watanabe TAA and Jiménez-Montaño M A 2001 Effective normalization of complexity measurements for epoch length and sampling frequency Phys. Rev. E 64 16209-17
19. Rapp P E, Zimmerman I D, Vining E P, Cohen N, Albano A M and Jiménez-Montaño M A 1994 The algorithmic complexity of neural spike trains increases during focal seizures J. Neurosci. 14 4731-9
20. Rieke F, Warland D, de Ruyter van Steveninck R and Bialek W 1998 Spikes: Exploring the Natural Code (Boston, MA: MIT Press)
21. Sanchez-Vives M V, Nowak L G and McCormick D A 2000a Membrane mechanisms underlying contrast adaptation in cat area 17 in vivo, J. Neurosci. 20 4267-85
22. Sanchez-Vives M V, Nowak L G and McCormick D A 2000b Cellular mechanisms of long-lasting adaptation in visual cortical neurons in vitro, J. Neurosci. 20 4286-99
23. Shannon C E 1948 A mathematical theory of communication Bell Syst. Tech. J. 27 379-423, 623-56
24. Skottun B C, DeValois R L, Grosof D H, Movshon J A, Albrecht D G and Bonds A B 1991 Classifying simple and complex cells in the basis of response modulation Vis. Res. 31 1079-86
25. Victor J D 2002 Binless strategies for estimation of information from neural data Phys. Rev. E 66 51903-17
26. Victor J D and Purpura K P 1997 Metric space analysis of spike trains: theory, algorithms and application Network 8 127-64
27. Wang X J, Liu Y, Sanchez-Vives M V and McCormick D A 2003 Adaptation and temporal decorrelation by single neurons in the primary visual cortex J. Neurophys. 89 (2) at press
28. Wyner A D, Ziv J and Wyner A J 1998 On the role of pattern matching in information theory IEEE Trans. Inf. Theory 44 2045-56
29. Zador A 1998 Impact of synaptic unreliability on the information transmitted by spiking neurons J. Neurophysiol. 79 1219-29
30. Ziv J 1978 Coding theorems for individual sequences IEEE Trans. Inf. Theory 24 405-12
31. Ziv J and Lempel A 1978 Compression of individual sequences via variable-rate coding IEEE Trans. Inf. Theory 24 530-6