Computational model of cellular plasticity

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The primary goal of this research project is to develop a mathematical model of the light-elicited generator potential and spike generation in the photoreceptors of Hermissenda. Paviovian conditioning of Hermissenda results in the modification of several K+ conductances in identified cells of the pathway supporting the conditioned stimulus (CS). The preparation is especially well suited for a biophysical analysis since the voltage- and light-dependent conductances of identified photoreceptors have been characterized in some detail with voltage-clamp techniques. In addition, conventional cellular neurophysiological techniques have identified several neural correlates of conditioning in the identified photoreceptors of the CS pathway. These recent results provide an opportunity to directly relate biophysical modifications in identified cells to neural correlates of conditioning that are expressed by changes in excitability and synaptic strength. The application of computer simulations of the photoreceptors would be used to help elucidate the role of diverse biophysical changes to the modification of excitability and synaptic strength in identified cells of conditioned animals. The proposal consists of the following four specific aims: 1) Use the voltage and light-dependent conductances of the photoreceptors derived from voltage-clamp studies to develop a quantitative model of the light-elicited generator potentials; 2) Use the quantitative model to examine the correspondence between modifications in various membrane conductances produced by conditioning and the modification of generator potentials and spike frequency predicted by the conductance changes; 3) Use the quantitative model to identify conductance changes that may contribute to the modification of generator potentials and spike frequency produced by conditioning; and 4) Develop a network model of CS pathway activity that incorporates known changes in excitability in identified cells and changes synaptic strength at identified synaptic connections. These duties will provide important insights into our understanding of the physiology of learning and memory.