Sequential estimation of gating variables from voltage traces in single-neuron models by particle filtering

This paper addresses the problem of inferring voltage traces and ionic channel activity from noisy intracellular recordings in a neuron. A particle filtering method with optimal importance density is proposed to that aim, with the benefits of on-line estimation methods and Bayesian filtering theory. The method is applied to an inaccurate Morris-Lecar neuron model without loss of generality. Simulation results show the validity of the approach, where it is observed that theoretical estimation bounds are attained.