An Algorithmic Method for Reducing Conductance-based Neuron Models

Although conductance-based neural models provide a realistic depiction of neuronal activity, their complexity often limits effective implementation and analysis. Neuronal model reduction methods provide a means to reduce model complexity while retaining the original model's realism and relevance. Such methods, however, typically include ad hoc components that require that the modeler already be intimately familiar with the dynamics of the original model. We present an automated, algorithmic method for reducing conductance-based neuron models using the method of equivalent potentials (Kelper et al., Biol Cybern 66(5): 381

• Type: Journal
• Year: 2006
• Where: BC
• Authors: Michael E. Sorensen, Stephen P. DeWeerth
• Keywords: BC 2006, Conductance-based Neural Models, Neuronal Model Reduction, Original Model