Identification of mechanisms for motor pattern selection during multimodal sensory integration.

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Central pattern generators are networks or individual nerve cells in animals and humans that govern vital functions of the body, including breathing, swallowing and chewing. Their activity is robust, but switches between different states when body or environmental conditions change. Such switches can be achieved by control neurons that release neuromodulators - chemical substances that dramatically alter network behavior. Control neurons collect information from all senses, but their conjoint activity and how their activity leads to appropriate behavioral responses is unknown. This proposal asks how sensory information is encoded by these nerve cells and how appropriate behavioral responses are selected. The hypothesis that appropriate behavioral responses are encoded in the population activity of control neurons will be tested in the stomatogastric nervous system of crabs, a powerful model system with unique access to neurons. The researchers will use a combination of high-end optical imaging and intracellular electrophysiology to measure and manipulate most control neurons while they select the appropriate behaviors during sensory stimulation. Expected results are that the encoding of sensory stimuli differs between different sensory inputs and that these differences are necessary and sufficient to cause switches in behavior. The proposed research provides comprehensive training for students at different stages of their careers (undergraduate, graduate and postdoc) and teaches a variety of established and recently developed scientific techniques. It will open new lines of research in systems where the investigation of control networks is not feasible due to the sheer number of neurons. Revealing the mechanisms of behavioral selection is crucial to our understanding of nervous system function and, ultimately, a prerequisite for the treatment of sensory disorders such as autism and learning disabilities. This project will also instruct engineers about ways of decision making without dedicated pathways and may thus lead to new and efficient neural networks in machines and robots.