Marcus Kaiser

• In this work, I studied the organization, development and robustness of cortical, as well as other biological networks, using methods of network analysis. First, I examined the organization of neural systems and important constraints for shaping them. Area positions in the macaque cortical as well as the C. elegans neural networks could be rearranged so that total wiring length can be reduced by up to 64% of the original value. Although causing such non-optimal wiring, long-distance connections help to minimize the number of intermediate nodes, which leads to lower time delay, less interference, and higher synchrony in the network. Second, I observed growth of networks in space. I designed a developmental algorithm that takes into account distance between nodes and which yields networks that are alike various spatial networks from metabolic networks to the German highway system. Such spatial growth can generate networks that are comparable to cat and macaque cortical networks. In addition, the inclusion of time windows for development can lead to a defined cluster architecture. Finally, I analyzed the measurement of robustness in biological networks as well as possible underlying causes for high robustness towards unspecific removal of edges or nodes. The existence of clusters and of highly-connected nodes results in enhanced average-case robustness after removal of edges or nodes. Using multiple lesions, I found that in some cases the effect differed from the effect that was predicted from single lesions. Therefore, multiple lesion analysis could become a framework to predict or explain 'unexpected' effects of experimental lesions (or multiple gene knock-outs in metabolic systems).