CAREER: From Connectome to Behavior: Computational Models of Multifunctional Neural Circuits in C. elegans

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How an animal flexibly coordinates multiple behaviors as a cohesive unit is one of the central problems of neuroscience; multifunctionality has also been recognized as one of the fundamental challenges in the development of a general artificial intelligence. Although the ability of neural circuits to flexibly reconfigure is widespread among organisms, most studies of the neural basis of behaviors focus on isolated circuits and individual behaviors. Studies that consider multifunctional circuitry tend to focus on the switching between distinct patterns of activity, with little insight into multifunctional sensorimotor integration. With the increasing amount of anatomical, physiological and behavioral data being generated, a computational modeling framework to understand the neural basis of behavior is essential. The goal of this project is to model multiple neural circuits that have been identified in isolation and to integrate them into a single model to better understand how multifunctionality arises in sensory-driven behavioral circuits. This project is an important step toward the long-term goal of developing a behaviorally-functional brain-body-environment model of a living organism at the level of individual neurons. The cross-disciplinary methodologies developed from this project will serve as a springboard for understanding multifunctional circuits in living organisms as well as for generating artificial systems capable of robustly and efficiently performing multiple functions. The project focuses specifically on modeling and analyzing the circuits responsible for the wide range of spatial orientation behaviors in the nematode Caenorhabditis elegans. This model organism is a uniquely qualified target for integrated computational modeling of a complete animal because of the breadth of information known about its genetics, development, anatomy, and behavior. Despite this substantial knowledge, information about the electrophysiological properties of its nervous system is less complete. The project aims to constrain the model by what is known from the anatomy and physiology of the organism with reasoned simplifications about its body and environment. Then, stochastic optimization will be used to fill in electrophysiological unknowns such that the model produces behavior that matches what has been observed, including the effect of neural manipulations on behavior. The result of optimization will not be a unique model, but rather an ensemble of models that are consistent with current knowledge of the system. Each of these possibilities represents a testable hypothesis for C. elegans. The next step in the project will be to analyze the structure of this ensemble to formulate the key experiments that can distinguish between the various classes of possibilities in the worm. The results of such experiments can then be used as additional constraints for subsequent optimizations in an iterative cycle of model refinement. Besides the generation of experimentally-testable predictions that are specific to C. elegans, through the analysis of the ensemble of models, the project aims to discover general principles for how multifunctional circuits operate in living organisms more broadly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.