Balance between efficiency and stability in a neural circuit model of the Drosophila brain
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1
National Tsing Hua University, Institute of Systems Neuroscience, Taiwan
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. Although much has being learned about the functions of individual sensory systems, how do they operated and integrated at the brain-wide level in Drosophila remains poorly understood. To provide a platform for studying computation of the fly nervous system, we initiated the Flysim project with a goal to construct a full-brain neural circuit model with a single-neuron resolution based on the data obtained from the flycircuit database.
Although containing only ~28,000 neurons (account for 22% of the fly brain) at the current stage, our fly brain model already exhibits unexpectedly rich dynamics. Due to the strongly recurrent excitation, the brain model is inherently unstable and epilepsy-like activity frequently arose from the baseline state. We further found that the epilepsy-like activity cannot be suppressed by solely strengthening the inhibitory synapses but can be eliminated by implementing short-term depression. However, depressed synapses strongly attenuate the propagation of signals and lead to an inefficient neural circuit. Therefore, a functioning nervous system requires fine balance between stability and efficiency. Additional analyses revealed that the epilepsy-like activity originates from a group of specific brain regions that are functionally less understood but seem to play roles in integrating signals from different sensory modules. By a large-scale screening through the inhibitory system of the brain model, we further discovered that the epilepsy-like activity can be suppressed by a small number of inhibitory neurons if they are manually activated. Interestingly, the epilepsy-suppressing ability of these neurons does not come from their inhibitory strength, but from their unique innervating patterns over the excitatory system. In conclusion, despite being in an early stage of the model development, our fly brain model has already shown its strength in providing insights into brain-wide neural dynamics which may not be revealed by analyzing the random or simple small-world neural networks.
Acknowledgements
This work is supported by the Ministry of Science and Technology and by the Ministry of Education, Taiwan. We thank the National Center for High-performance Computing for providing computational resources and Dr. Ann-Shyn Chiang for helpful comments.
Keywords:
Drosophila,
Epilepsy,
network stability,
fruit fly,
balanced networks,
Efficiency,
short-term depression,
short-term synaptic plasticity,
Inhibitory Control
Conference:
Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.
Presentation Type:
Poster, to be considered for oral presentation
Topic:
Computational neuroscience
Citation:
Wang
C,
Huang
Y and
Lo
C
(2015). Balance between efficiency and stability in a neural circuit model of the Drosophila brain.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2015.
doi: 10.3389/conf.fnins.2015.91.00035
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Received:
07 Apr 2015;
Published Online:
05 Aug 2015.
*
Correspondence:
Dr. Chung-Chuan Lo, National Tsing Hua University, Institute of Systems Neuroscience, Hsinchu city, US & Canada only, 300, Taiwan, cclo@life.nthu.edu.tw