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The importance of Network Topology again… at [PLoS]. Abstract: […] The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems. […] (*)

(*) in Hermundstad AM, Brown KS, Bassett DS, Carlson JM, 2011 Learning, Memory, and the Role of Neural Network Architecture. PLoS Comput Biol 7(6): e1002063. doi:10.1371/journal.pcbi.1002063

How we engage the dance between structure and flow becomes our unique creative signature“, ~ Michelle James, VA, USA, 2010.

[…] Abstract.: The problem of sending the maximum amount of flow q between two arbitrary nodes s and t of complex networks along links with unit capacity is studied, which is equivalent to determining the number of link-disjoint paths between s and t. The average of q over all node pairs with smaller degree kmin is <q>=kmin ~= c.kmin for large kmin with c a constant implying that the statistics of q is related to the degree distribution of the network. The disjoint paths between hub nodes are found to be distributed among the links belonging to the same edge-biconnected component, and q can be estimated by the number of pairs of edge-biconnected links incident to the start and terminal node. The relative size of the giant edge-biconnected component of a network approximates to the coefficient c. The applicability of our results to real world networks is tested for the Internet at the autonomous system level. […], in D.-S. Lee and H. Rieger, “Maximum flow and topological structure of complex networks“, EPL Journal, Europhysics Letters, Vol. 73, Number 3, p.471, 2006. [link] …

“Artificial Life 101” lesson nº1: emerge yourself a experimental “fire” and observe it carefully till the end.

… The other day I decided to work for almost 48 hours in a row and at the end… to sleep a few hours. When I woke up… – the day (e.g. “flow“) was almost gone, and night (e.g. “structure“) approaching fast -. So I decided, to grab the last sun rays… ; growing up, I guess…

[...] People should learn how to play Lego with their minds. Concepts are building bricks [...] V. Ramos, 2002.

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