Optimal control methods for nonlinear parameter estimation in biophysical neuron models.

ABSTRACT: Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neuron models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint pa

SUBMITTER: Kadakia N 

PROVIDER: S-EPMC9514669 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature