Name
Title | ||
|---|---|---|
Functional Holography Of Complex Networks Activity - From Cultures To The Human Brain |
Abstract | ||
|---|---|---|
A functional holography (FH) approach is introduced for analyzing the complex activity of biological networks in the space of functional correlations. Although the activity is often recorded from part of the nodes only, the goal is to decipher the activity of the whole network. This is why the analysis is guided by the "whole in every part" nature of a holograms-a small part of a hologram will generate the whole picture but with lower resolution. The analysis is started by constructing the space of functional correlations from the similarities between the activities of the network components using a special collective normalization or affinity transformation. Using dimension reduction algorithms like PCA, a connectivity diagram is generated in the 3-dimensional space of the leading eigenvectors of the algorithm. The network components are positioned in the 3-dimensional space by projection on the eigenvectors and connect them with colored lines that represent the similarities. Temporal (causal) information is superimposed by coloring the node's locations according to the temporal ordering of their activities. Utilizing the analysis, the existence of hidden manifolds with simple yet characteristic geometrical and topological features in the complex biological activity was discovered from cultured networks to the human brain. These findings could be a consequence of the analysis being consistent with a new holographic principle by which biological networks regulate their complex activity. (c) 2005 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1002/cplx.20065 | COMPLEXITY |
Keywords | Field | DocType |
functional holography, biological networks, similarity matrices, principal component algorithm | Holography,Normalization (statistics),Dimensionality reduction,Biological network,Diagram,Complex network,Artificial intelligence,Eigenvalues and eigenvectors,Machine learning,Mathematics,Manifold | Journal |
Volume | Issue | ISSN |
10 | 3 | 1076-2787 |
Citations | PageRank | References |
5 | 0.55 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Itay Baruchi | 1 | 18 | 3.66 |
| Vernon L. Towle | 2 | 20 | 2.16 |
| Eshel Ben-Jacob | 3 | 170 | 25.37 |