Event Abstract

Network architecture and information propagation in protocerebral bridge of Drosophila central complex

  • 1 Institute of Systems Neuroscience, National Tsing Hua University, Taiwan
  • 2 Brain Research Center, National Tsing Hua University, Taiwan
  • 3 Department of Physics, Tunghai University, Taiwan

The central complex (CX), which consists of four neuropils located in the central brain of insects, is characterized by a complex but highly organized and repetitive circuit architecture. Furthermore, CX has been suggested to participate in a range of functions including spatial working memory, sensory-motor transformation and motor control. However, how these functions are implemented and realized by the complex neural circuits in CX remains unclear. As a first step toward understanding of the functions of the CX neural circuits, we mathematically analyzed connectivity of 662 Drosophila neurons which innervate one of the CX neuropils, the protocerebral bridge. Specifically, each neuron is represented as a high-dimensional innervation vector with each dimension corresponding to a subregion of CX. We found that the seemly complex innervation patterns of the neurons are highly structured and the whole network can be generated or even predicted by applying a generator matrix on a small set of initial neurons. The result implies that the development of the complex CX neural network can be highly efficient because it can be driven by a small set of genes that encode the simple rules, or the generator matrices.
We further investigated a small set of observed neurons with innervation patterns that cannot be generated from the generator matrices. To determine whether these “special” neurons play specific roles in information transduction, we compared the network constructed by neurons from observed data and the network generated from the mathematical model (the generator matrices). Specifically, we studied how signals propagate from a given input neuron to a given output neuron through multiple intermediate neurons. We found that the observed network is characterized by strong recurrence that is several folds stronger than that of the model network for specific input-output neuron pairs. We further identified that only two specific neurons in EIP class are responsible for the major changes in the network recurrence which greatly increases the complexity of network computation. Further analysis indicated that the unique innervation pattern of these neurons plays a key role in maximizing the network recurrence for the specific input-output neuron pairs. The result suggests that a small number of specially designed neurons can greatly improve the complexity of the neural computation. Therefore, our work provides insights into the complex organization of CX neural circuits and may generate specific predictions that can be tested experimentally.

Figure 1


This work is supported by Aim for the Top University Project of the Ministry of Education, Taiwan. We also thank National Center for High-performance Computing for providing FlyCircuit database.


Chiang, A.-S., Lin, C.-Y., Chuang, C.-C., Chang, H.-M., Hsieh, C.-H., Yeh, C.-W., Shih, C.-T., Wu, J.-J., Wang, G.-T., Chen, Y.-C., et al. (2010). Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Curr Biol. Available at: http://www.ncbi.nlm.nih.gov/pubmed/21129968 [Accessed December 16, 2010].

Keywords: Drosophila, protocerebral bridge, central complex, Recurrent networks, network architecture, connectivity

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: General neuroinformatics

Citation: Chang P, Shih C and Lo C (2013). Network architecture and information propagation in protocerebral bridge of Drosophila central complex. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00009

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Received: 08 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence:
Prof. Chi-Tin Shih, Department of Physics, Tunghai University, Taichung, 40704, Taiwan, ctshih@thu.edu.tw
Dr. Chung-Chuan Lo, Institute of Systems Neuroscience, National Tsing Hua University, Hsinchu, 30013, Taiwan, cclo@life.nthu.edu.tw