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Generating biologically detailed models of neurons is an important goal for modern neuroscience. Unfortunately, constraining parameters within biologically detailed models can be difficult, leading to poor model predictions, especially if such models are extended beyond the specific problems for which they were designed. This major obstacle can be partially overcome by numerical optimization and detailed exploration of parameter space. These processes, which currently rely on central processingdoi:10.1101/727560 fatcat:epfypwuuerb7pdaga7sls43rti