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The Flysim project – persistent simulation and real-time visualization of fruit fly whole-brain spiking neural network model

Yu-Chi Huang1, Cheng-Te Wang1, Guo-Tzau Wang2, Ta-Shun Su1, Pao-Yueh Hsiao1, Ching-Yao Lin2, Chang-Huain Hsieh2, Hsiu-Ming Chang3 and Chung-Chuan Lo1*
  • 1 National Tsing Hua University, Institute of Systems Neuroscience, Taiwan
  • 2 National Center for High-Performance Computing, Taiwan
  • 3 National Tsing Hua University, Brain Research Center, Taiwan

Computer simulations play an important role in testing hypotheses, integrating knowledge and providing predictions of neural circuit functions. While lots efforts have been put into simulating primate or rodent brains, fruit fly (Drosophila melanogaster) is becoming a promising model animal in computational neuroscience for its small brain size, complex cognitive behavior and abundant data from genes to circuits. With the expectation that most neurons in Drosophila brain will be mapped out in the next few years, we propose the Flysim project with an aim to establish a data-driven neural network model of Drosophila. The project consists of four components: 1) analyzing neuronal data from Flycircuit (http://www.flycircuit.tw/) (Chiang, et. al. Current Biology, 2011) database, 2) building a whole-brain spiking neural network model, 3) performing persistent neural network simulations and 4) visualizing the simulated brain activity in real time. To this end, we first developed computer algorithms to identify axonal and dendritic domain for each neuron in the database (the SPIN project)(Lee, et al. Neurinformatics, 2014), predicted potential synapses between neurons and constructed the brain-wide connectome. Next, we developed a highly flexible neural network simulator which is capable of receiving control comments online from remote users. Finally, we developed an interface to pass the simulated data to a webserver for live demonstration. Users can monitor the neural activity in a 3-D virtual fly brain (Figure 1) and issue control comments such as stimulus onset/offset in real time. Currently, the Flysim simulator supports the leaky integrate-and-fire neuron model and ionotropic synapses including Ach, AMPA, NMDA and GABAA. For the sake of flexibility, we used a modular design for the system and the current CPU-based simulation may be replaced by a GPU-based system such as Neurokernel. We have built a “primitive” Drosophila brain network of ~22,000 neurons, which account for roughly 20% of the fly brain. Despite being at its early stage, this cellular-level brain network model allows us to study some of the fundamental properties of neural networks including balance of excitation and inhibition, critical behavior, long-term stability and plasticity.

Figure 1
Image

Acknowledgements

This work is supported by the National Science Council and by the Ministry of Education, Taiwan. We thank the National Center for High-performance Computing for providing computational recourses and Dr. Ann-Shyn Chiang for helpful comments.

References

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. (2011). Three-Dimensional Reconstruction of Brain-wide Wiring Networks in Drosophila at Single-Cell Resolution. Current Biology, 21(1), 1–11. doi:10.1016/j.cub.2010.11.056

Lee, Y.-H., Lin, Y.-N., Chuang, C.-C., Lo, C.-C. (2014) SPIN: A Method of Skeleton-Based Polarity Identification for Neurons. Neuroinformatics doi:10.1007/s12021-014-9225-6

Keywords: Drosohpila, Computer Simulation, spiking network model, connectome, neural circuit

Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.

Presentation Type: Demo, to be considered for oral presentation

Topic: Large-scale modeling

Citation: Huang Y, Wang C, Wang G, Su T, Hsiao P, Lin C, Hsieh C, Chang H and Lo C (2014). The Flysim project – persistent simulation and real-time visualization of fruit fly whole-brain spiking neural network model. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00043

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Received: 27 Apr 2014; Published Online: 04 Jun 2014.

* Correspondence: Prof. Chung-Chuan Lo, National Tsing Hua University, Institute of Systems Neuroscience, Hsinchu city, US & Canada only, 300, Taiwan, cclo@life.nthu.edu.tw