Functional neural mapping of food search behavior for C. elegans using genetic tools and computational modeling
Grantor: DPU RFUMS
This funding supports research into neural mapping of the nematode C. elegans during food search behavior. A major goal of neuroscience is to understand how complex behaviors arise from neural networks. Because of enormous complexity in mammalian brains, researchers resorted to simple organisms whose neural structure is relatively simple. With a mere 302 neurons, the nematode C. elegans provides many advantages to investigate the neural basis of behaviors, including well-defined anatomical synaptic connections and invariable positions of neurons in different animals. Yet, studies by us showed that C. elegans food search behavior is highly conserved with those of other organisms in behavioral patterns and neural encoding.
The project researchers are specifically focusing on the networks of interneurons that interface sensory and motor neurons in food search behavior. They will individually abolish the functions of approximately a half of the interneurons (40 neurons), and record their food search behaviors at a high frame rate for a long period of time. The resulting data will be analyzed for complex search behavioral patterns as well as simple motor patterns using advanced machine learning algorithms, such as supervised, unsupervised, and deep learning. Based on the analysis, the interneurons will be clustered according to features. This cluster information of the interneurons, in conjunction with the anatomical connection map, will be used for constructing a map that delineates functional connections between interneurons. The new map will be further confirmed or elaborated by ablating two neurons in the same clusters, and by optogenetically modulating interneurons. Given that basic modules of neural networks that control behavior are conserved across species, the study will contribute to an understanding of functional neural networks in other higher organisms.