Complejidad

1. Complejidad Geofisica Biología MacroEconomía Psicologia Meteorología Ecología Dante R. Chialvo Email: dchialvo@ucla.edu www.chialvo.net

2. Siempre que vemos Complejidad vemos No-Uniformidad P (kilos) Log P ($) kilos Log($) Complejidad es igual a No-Uniformidad La estadistica que aprendimos describe la uniformidad (gaussianas) La naturaleza es NO HOMOGENEA!!!, por donde se la mire Ejemplo: distribución de peso versus distribución de pesos $ “muchas formas” “una forma”

3. “Fractals” Que son? Como estudiarlos? por que nos pueden importar? Material extraido del libro “Introduction to Fractals” Larry S. Liebovitch Lina A. Shehadeh

4. How fractals CHANGE the most basic ways we analyze and understand experimental data.

5. Non-Fractal

6. Fractal

7. El universo es Fractal http://pil.phys.uniroma1.it/debate.html

8. Non - Fractal Size of Features 1 cm 1 characteristic scale

9. Fractal Size of Features 2 cm 1 cm 1/2 cm 1/4 cm many different scales

10. Fractals “Self-Similarity” Auto-similaridad

11. Self-Similarity • Pieces resemble the whole. Water Water Water Land Land Land

12. Sierpinski Triangle

13. Branching Patterns air ways blood vessels in the retina in the lungs Family, Masters, and Platt 1989 Physica D38:98-103 Mainster 1990 Eye 4:235-241 West and Goldberger 1987 Am. Sci. 75:354-365

14. Blood Vessels in the Retina

15. PDF - Probability Density Function HOW OFTEN there is THIS SIZE Straight line on log-log plot = Power Law

16. Statistical Self-Similarity The statistics of the big pieces is the same as the statistics of the small pieces.

17. Currents Through Ion Channels

18. Currents Through Ion Channels

19. Currents Through Ion Channels ATP sensitive potassium channel in cell from the pancreas Gilles, Falke, and Misler (Liebovitch 1990 Ann. N.Y. Acad. Sci. 591:375-391) FC = 10 Hz 5 sec 5 pA FC = 1k Hz 5 msec

20. Closed Time Histograms potassium channel in the corneal endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68 Number of closed Times per Time Bin in the Record Closed Time in ms

21. Closed Time Histograms potassium channel in the corneal endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68 Number of closed Times per Time Bin in the Record Closed Time in ms

22. Closed Time Histograms potassium channel in the corneal endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68 Number of closed Times per Time Bin in the Record Closed Time in ms

23. Closed Time Histograms potassium channel in the corneal endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68 Number of closed Times per Time Bin in the Record Closed Time in ms

24. Fractals Scaling

25. Scaling The value measured depends on the resolution used to do the measurement.

26. How Long is the Coastline of Britain? Richardson 1961 The problem of contiguity: An Appendix to Statistics of Deadly Quarrels General Systems Yearbook 6:139-187 AUSTRIALIAN COAST 4.0 CIRCLE SOUTH AFRICAN COAST Log10 (Total Length in Km) 3.5 GERMAN LAND-FRONTIER, 1900 WEST COAST OF BRITIAN 3.0 LAND-FRONTIER OF PORTUGAL 3.5 1.0 2.0 3.0 1.5 2.5 LOG10 (Length of Line Segments in Km)

27. Genetic Mosaics in the Liver P. M. Iannaccone. 1990. FASEB J. 4:1508-1512. Y.-K. Ng and P. M. Iannaccone. 1992. Devel. Biol. 151:419-430.

28. k in Hz eff effective kinetic rate constant 70 pS K+ ChannelCorneal Endothelium Liebovitch et al. 1987 Math. Biosci. 84:37-68. 1000 1-D k = A t eff eff 100 10 1 1 10 100 1000 effective time scale t in msec eff

29. Fractal Approach New viewpoint: Analyze how a property, the effective kinetic rate constant, keff, depends on the effective time scale, teff, at which it is measured. This Scaling Relationship: We are using this to learn about the structure and motions in the ion channel protein.

30. Scaling scaling relationship: much more interesting one measurement: not so interesting one value Logarithm of the measuremnt Logarithm of the measuremnt slope Logarithm of the resolution used to make the measurement Logarithm of the resolution used to make the measurement

31. Fractals Statistics

32. Not Fractal

33. Not Fractal

34. Gaussian Bell Curve “Normal Distribution”

35. Fractal

36. Fractal

37. Non - Fractal Mean pop More Data

38. The Average Depends on the Amount of Data Analyzed

39. The Average Depends on the Amount of Data Analyzed each piece

40. Ordinary Coin Toss Toss a coin. If it is tails win $0, If it is heads win $1. The average winnings are: 2-1.1 = 0.5 1/2 Non-Fractal

41. Ordinary Coin Toss

42. Ordinary Coin Toss

43. St. Petersburg Game (Niklaus Bernoulli) Toss a coin. If it is heads win $2, if not, keep tossing it until it falls heads. If this occurs on the N-th toss we win $2N. With probability 2-N we win $2N. H $2 TH $4 TTH $8 TTTH $16 The average winnings are: 2-121 + 2-222 + 2-323 + . . . = 1 + 1 + 1 + . . . = Fractal

44. St. Petersburg Game (Niklaus Bernoulli)

45. St. Petersburg Game (Niklaus Bernoulli)

46. Non-Fractal Log avg density within radius r Log radius r

47. Fractal Meakin 1986 In On Growthand Form: Fractal and Non-Fractal Patterns in Physics Ed. Stanley & Ostrowsky, Martinus Nijoff Pub., pp. 111-135 Log avg density within radius r 0 .5 -1.0 -1.5 -2.0 -2.5 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 0 Log radius r

48. Electrical Activity of Auditory Nerve Cells Teich, Jonson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 action potentials voltage time

49. Electrical Activity of Auditory Nerve Cells Teich, Jonson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 Divide the record into time windows: Count the number of action potentials in each window: 2 6 3 1 5 1 Firing Rate = 2, 6, 3, 1, 5,1

50. Electrical Activity of Auditory Nerve Cells Teich, Johnson, Kumar, and Turcott 1990 Hearing Res. 46:41-52 Repeat for different lengths of time windows: 8 4 6 Firing Rate = 8, 4, 6