Inhibitory Regulation of Behavior and Learning

Wayne State University, Detroit MI

Investigators

Abstract

Neural networks are dynamic systems that enable organisms to adapt to and learn about complex and variable environments. At the core of this adaptive process is the phenomenon of synaptic plasticity, an alteration in the efficacy of a synapse due either to intrinsic processes (e.g. activity of the neuron) or extrinsic processes (e.g. neuromodulators released by other neurons). The functional significance of synaptic plasticity will be examined by focusing upon a specific set of identified inhibitory neurons within a well-defined neural circuit in the marine mollusc Aplysia. Inhibitory processes can have profound consequences on neural function, yet have received comparatively little experimental emphasis. By focusing on a specific synaptic process in a specific set of inhibitory neurons, these studies will allow for a simultaneous exploration of the function of both synaptic plasticity and inhibition in behavioral regulation. Two basic issues will be examined, integrating experiments at the level of synapses with the level of behavior: (1) The role of synaptic inhibition in behavioral regulation will be explored, examining the hypothesis that plasticity of inhibition may serve as a dynamic gain control mechanism that can regulate behavior across rapidly-changing environmental conditions. (2) The role of inhibition in learning will be explored, by examining the hypothesis that plasticity of inhibition may serve to dynamically regulate the capacity for neural networks to express learning-related changes. Information obtained from this project could help to elucidate general computational principles utilized by a neural networks to generate adaptive modifications, which can potentially be employed in algorithms usable by other computational or information processing devices (e.g. smart machines). From a theoretical perspective, it could address fundamental questions concerning the nature of adaptive modifications within neural networks that are required for the expression of different forms of learning and memory.