Accuracy in Estimating Motor Commands Using Neuronal Population Coding

Abstract

We consider the problem of estimating motor commands from an ensemble of neuronal activities. The population vector algorithm proposed by Georgopoulos provides largely biased estimations when preferred directions of neurons are non-uniformly distributed. To improve this, various decoding methods have been proposed. However, dependence of decoding accuracy on the motor command and other features of neural activities, such as baseline firing rates or amplitudes of tuning curves, have not been quantitatively discussed. In this study, we propose a new method to estimate the motor command in the maximum likelihood estimation framework, which is analytically tractable. We find that the estimation accuracy is independent of the motor command. Using our estimation method, we can estimate the motor command with equal accuracy in all directions.