Dynamic neural field optimization using the unscented Kalman filter

Dynamic neural fields have been proposed as a continuous model of a neural tissue. When dynamic neural fields are used in practical applications, the tuning of their parameters is a challenging issue that most of the time relies on expert knowledge on the influence of each parameter. The methods that have been proposed so far for automatically tuning these parameters rely either on genetic algorithms or on gradient descent. The second category of methods requires to explicitly compute the gradient of a cost function which is not always possible or at least difficult and costly. Here we propose to use unscented Kalman filters, a derivative-free algorithm for parameter estimation, which reveals to efficiently optimize the parameters of a dynamic neural field.