NSF Award
2113120
The integrated function of the human brain allows every individual human to have unique thoughts, perceptions, memories, and actions. One of the grand scientific challenges of our time is to mechanistically understand how collections of neurons accomplish these incredibly sophisticated functions. However, it turns out, that this is a daunting task that requires a comprehensive understanding of a brain at every level of complexity, from molecules to neurons, the circuits and systems they form, and the underlying computational principles. To reach the goal of understanding the brain, we must first be able to understand and simulate simpler brains like the nervous system of the nematode worm Caenorhabditis elegans. Given its simplicity, scientists have been able to map the physical wiring of the entire nervous system – the connectome – in the attempt to reconstruct the worm brain. However, without knowing the biophysical properties of the diverse neuron types and the activity pattern they produce, scientists have been unable to generate a unifying model that explains how the brain of this simple worm works. This project aims to address this problem by comprehensively characterizing the biophysical properties of a large portion of C. elegans neurons and constructing accurate mathematical models for these neurons and the circuits they constitute. The goal is to reproduce neural activity patterns in different neuron types and neural circuits, and eventually simulate how the worm brain generates simple behaviors. <br/><br/>To accomplish this goal, the researchers will take a systematic approach of recording from 42 selected neuron types in C. elegans using electrophysiology. This set of neurons was selected based on their known function in multiple well-studies behavioral circuits including chemosensory, mechanosensory, thermosensory, nociceptive circuits, and downstream integrating and motor circuits. Detailed electrophysiological parameters, and recordings of neural dynamics will be obtained from experiments for each neuron type and deposited into a public database available for the scientific community. Following the comprehensive characterization of these neurons, the researchers will model the single neuron dynamics and currents according to the Hodgkin-Huxley equations. Novel machine learning methodology based on Deep Reinforcement Learning (Deep RL) will be developed to find parameter candidates such that they fit the equations to satisfy multiple optimized objectives in the recordings. Optimal single neuron models will subsequently be integrated into a connectome-based whole-brain framework to develop anatomically and biophysically correct circuit models. The robustness of these dynamic models will be tested with various computational ablations. This exploratory study is a proof-of-principle test case to evaluate the impact of biophysical single neuron models on the full-scale whole-brain electrophysiome simulation and provide initial insights into the level of abstraction possible for systemic modeling of the entire C. elegans nervous system. Ultimately, the knowledge gained from this project is expected to act as steppingstone for understanding and modeling more complex nervous systems.<br/><br/>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.
