Dipartimento d'Ingegneria

Montecchiani Fabrizio

Montecchiani Fabrizio

Website URL: http://mozart.diei.unipg.it/montecchiani/ Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Additional Details

  • Phone
    075 585 3794
  • Role
    Post-Doc
  • Area
    Ingegneria informatica - Computer engineering
Monday, 06 May 2013 13:11

The Drosophila Neural Network

Designing reliable networks is a fundamental issue in many application domains, such as transportation, computer networking and power supply. The reliability of a network is often described by its robustness under errors or attacks. Most “naturally evolved” networks have been shown to be scale-free. Furthermore, it has been shown that scale-free networks are typically robust to random failures but vulnerable to targeted attacks, opposite to random networks.

neural network 1

The presented research is the result of a collaboration among our research group, the Brain Research Center of the National Tsing Hua University of Taiwan and the National Center for High-performance Computing of Taiwan. Recently, the Brain Research Center of the National Tsing Hua University of Taiwan published a 3D image database for single neurons, called FlyCircuit (www.flycircuit.tw). This data source has been built from the brain of Drosophila Melanogaster  and it contains more than ten thousand neurons. Single neuron images were acquired and reconstructed into a standardized brain space so as to build a 3D connection atlas with physiological significance. Furthermore, the connectivity matrix raising from this neural network has been constructed. The main goal of our collaboration was to analyze this reconstructed neural network, exploiting our different expertise in biology, bioinformatics, information visualization and algorithm engineering. Indeed, this research required an interdisciplinary collaboration involving systematic image collection, coordinates unification, algorithms for graph analysis and visualization.

We demonstrate the resiliency of this reconstructed neural network under various errors and attacks. Most importantly, our results showed that the robustness of the network improved throughout the network’s development. We ran several experiments to measure the network's robustness and we adopted a visualization algorithm for clustered networks to compare the network structure before and during an attack. The information provided by the drawings of the network gave us a fundamental support in designing the experiments and analyzing the results.

Our findings can provide new clues for successful network construction and shed new light on the generation process of this neural network. Being able to understand and replicate the conditions behind such a robust structure would have a great impact in designing reliable networks in different application domains. 

drosophila neural network

Related publications:

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Hsiu-Ming Chang, Ann-Shyn Chiang, Walter Didimo, Ching-Yao Lin, Giuseppe Liotta and Fabrizio Montecchiani: On the Robustness of the Drosophila Neural Network. IEEE NSW 2013: 168-171

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