Neural network features distinguish chemosensory stimuli in Caenorhabditis elegans

Nervous systems extract and process information from the environment to alter animal behavior and physiology. Despite progress in understanding how different stimuli are represented by changes in neuronal activity, less is known about how they affect broader neural network properties. We developed a framework for using graph-theoretic features of neural network activity to predict ecologically relevant stimulus properties, in particular stimulus identity. We used the transparent nematode, Caenorhabditis elegans, with its small nervous system to define neural network features associated with various chemosensory stimuli. We first immobilized animals using a microfluidic device and exposed their noses to chemical stimuli while monitoring changes in neural activity of more than 50 neurons in the head region. We found that graph-theoretic features, which capture patterns of interactions between neurons, are modulated by stimulus identity. Further, we show that a simple machine learning classifier trained using graph-theoretic features alone, or in combination with neural activity features, can accurately predict salt stimulus. Moreover, by focusing on putative causal interactions between neurons, the graph-theoretic features were almost twice as predictive as the neural activity features. These results reveal that stimulus identity modulates the broad, network-level organization of the nervous system, and that graph theory can be used to characterize these changes.Author summary: Animals use their nervous systems to detect and respond to changes in their external environment. A central challenge in computational neuroscience is to determine how specific properties of these stimuli affect interactions between neurons. While most studies have focused on the neurons in the sensory periphery, recent advances allow us to probe how the rest of the nervous system responds to sensory stimulation. We recorded activity of neurons within the C. elegans head region while the animal was exposed to various chemosensory stimuli. We then used computational methods to identify various stimuli by analyzing neural activity. Specifically, we used a combination of population-level activity statistics (e.g., average, standard deviation, frequency-based measures) and graph-theoretic features of functional network structure (e.g., transitivity, which is the existence of strongly connected triplets of neurons) to accurately predict salt stimulus. Our method is general and can be used across species, particularly in instances where the identities of individual neurons are unknown. These results also suggest that neural activity downstream of the sensory periphery contains a signature of changes in the environment.​