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Functional Holography (FH) is a method of analysis designed to extract the maximum amount of functional information about the dynamical network as a whole unit.
Itay Baruchi and his Ph.D. supervisor, Eshel Ben-Jacob, introduced the Functional Holography (FH) methodology. The FH analysis was devised to study the dynamics of task performing dynamical networks (such as brain activity and neural networks,[1][2][3][4][5][6] and gene networks[7][8] or recorded data of dynamics system such as stock market parameters[9][10] or biological chips activities).[11]
The new approach is based on the realization that task-performing networks follow some underlying principles that should be reflected and therefore be detected in their activity. Where the analysis is designed to decipher the existence of simple causal motives that are expected to be embedded in the observed complex activity of the networks are noticeable. Many studies have applied the FH analysis to modeled and real networks or complex data (such as recorded brain activity, gene microarray data, antigen microarray data and even financial data) the characteristic geometrical and topological features are deciphered in the complex activity.
^Baruchi, I., Towle, V.L. and Ben-Jacob, E. (2005), Functional Holography of Complex Networks Activity From Cultures to the Human Brain, holography2.pdf Complexity, Volume 10, No 3, p. 38-51
^2. Baruchi, I., and Ben-Jacob, E. (2004), Functional Holography of Recorded Neuronal Networks Activity, hologrphy.pdf Neuroinformatics , Volume 2, Issue 3, p. 333-352
^Baruchi, I., grossman, D., Volman, V., Hunter, J., Towle, V. L., Ben-Jacob, E. (2006), Functional Holography Analysis: Simplifying the complexity of dynamical networks, In Chaos Focus Issue on Stability and Pattern Formation in Networks of Dynamical Systems edited by L. Pecora and S. Boccaletti Chaos 16, 015112
^4. Ben-Jacob, E., Doron, I., Gazit, T., Rephaeli, E., Sagher, O. and Towle, L. V. (2007), Mapping and assessment of epileptogenic foci using frequency-entropy templates, and Assessment published.pdf PHYSICAL REVIEW E 76, 051903
^T. Gazit, I. Doron, O. Sagher, M.H. Kohrman, V.L. Towle, M. Teicher, E. Ben-Jacob, (2011), Time-frequency characterization of electrocorticographic recordings of epileptic patients using frequency-entropy similarity: A comparison to other bivariate measures, and frequency entropy similarity.pdf J. Neuroscience Methods Vol. 194, pp 358-373
^Jacob, Y., Rapson, A., Kafri, M., Baruchi, I., Hendler T & Ben-Jacob, E. (2010), Revealing voxel correlation cliques by functional holography analysis of fMRI, Journal of Neuroscience Methods 191, pp. 126–137
^Madi, A., Friedman, Y., Roth, D., Regev, T., Bransburg-Zabary, S., Ben-Jacob, E. (2008), Genome Holography: Deciphering Function-Form Motifs from Gene Expression Data, Holography published.pdf PLoS ONE, vol 3 Issue 7
^8. Roth, D., Madi, A., Kenett, D.Y., Ben-Jacob, E. (2010), Gene Network Holography of the Soil Bacterium Bacillus subtilis , Chapter 10, pp 255-280 in G.Witzany (ed.), Biocommunication in Soil Microorganisms, Soil Biology Series Vol. 23 Springer-Verlag Berlin Heidelberg
^Shapira, Y., Kenett, D.Y., and Ben-Jacob, E. (2009), The Index Cohesive Effect on Stock Correlations., Index cohesive effect on stock market correlations_final.pdf Eur. Phys. J. B 72, 657-669
^Kenett. D, Shapira. Y, Madi. A, Bransburg-Zabary. S, Gur-Gershgoren.G & Ben-Jacob, E. (2010), Dynamics of Stock Market Correlations, AUCO Czech Economic Review 4, pp. 330–340
^Madi, A., Hecht, I., Bransburg- Zabary, S., Merbl, Y., Pick, A., Zucker-Toledano, M., Francisco, J. Quintana, Tauber, A. I., Cohen, I. R. and Ben-Jacob, E. (2009), Organization of the autoantibody repertoire in healthy newborns and adults revealed by system level informatics of antigen microarray data. of the autoantibody repertoire in healthy newborns and adults revealed by system.pdf PNAS, Vol. 106(34) pp 14484-14489
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