One of the many complex behaviors is the locomotion of living creatures that are controlled by a well-coordinated neural network and the neuromuscular junctions. It has been difficult to probe and understand this complex behavior in the larger animals. The simplicity of the invertebrate neuronal system has helped to identify system-level information to understand the biological process underlying how complex neuronal networks operate. In order to study latent motor neuron activities, we capture all the motor neurons present in the ventral cord of a larval 4 stage C. elegans. The animal is immobilized inside a meander-shaped microfluidic channel to mimic its natural body posture during crawling. A custom-designed confocal system with a flexible field of view is used to image the entire animal within 630 × 160
mum2 and using 430 nm optical resolutions. We characterized the basal activities of the entire motor neuron circuit in the anesthetic-free animal to identify oscillatory neuron responses using imaging rate of >5 volumes per second. To autonomously extract each neuron's signal, we denoise each volume via multiscale analysis and identify unique neurons based on a set of pixel-wise statistical and morphological features. An interactive software allows for human input and customizes neuron identification to correct for the errors made during automated analysis before each neural trace is extracted for further analysis. We observed sparse latent signaling patterns in the C. elegans motor activities that can be described well by a low-dimensional space, within some bound of error, to a high degree of precision.