Rapid detection and recognition of whole brain activity in a freely behaving Caenorhabditis elegans
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Advanced volumetric imaging methods and genetically encoded activity indicators have permitted a comprehensive characterization of whole brain activity at single neuron resolution in Caenorhabditis elegans. The constant motion and deformation of the mollusc nervous system, however, impose a great challenge for a consistent identification of densely packed neurons in a behaving animal. Here, we propose a cascade solution for long-term and rapid recognition of head ganglion neurons in a freely moving C. elegans. First, potential neuronal regions from a stack of fluorescence images are detected by a deep learning algorithm. Second, 2 dimensional neuronal regions are fused into 3 dimensional neuron entities. Third, by exploiting the neuronal density distribution surrounding a neuron and relative positional information between neurons, a multi-class artificial neural network transforms engineered neuronal feature vectors into digital neuronal identities. Under the constraint of a small number (20-40 volumes) of training samples, our bottom-up approach is able to process each volume - 1024 × 1024 × 18 in voxels - in less than 1 second and achieves an accuracy of 91% in neuronal detection and 74% in neuronal recognition. Our work represents an important development towards a rapid and fully automated algorithm for decoding whole brain activity underlying natural animal behaviors.
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