Statistical Analysis of the Non-stationarity of Neural Population Codes

Neural prosthetic technology has moved from the laboratory to clinical settings with human trials. The motor cortical control of devices in such settings raises important questions about the design of computational interfaces that produce stable and reliable control over a wide range of operating conditions. In particular, non-stationarity of the neural code across different behavioral conditions or attentional states becomes a potential issue. Non-stationarity has been previously observed in animals where the encoding model representing the mathematical relationship between neural population activity and behavioral variables such as hand motion changes over time. If such an encoding model is formed and learned during a particular training period, decoding performance (neural control) with the model may not be consistent during successive periods even when the same task is repeated. It is critical in both laboratory experiments and in clinical settings to be able to evaluate whether the representation of movement encoded by a neural population has changed or not. Such information can be used as a cue to retrain the system or as feedback to an adaptive decoding algorithm. To that end, we develop a statistical methodology to evaluate changes in the neural code over time using a generative probabilistic decoding model. The changes are evaluated by comparing the likelihoods of firing rates given similar distributions of 2D hand kinematics collected while a primate periodically performs a manual cursor control task. A comparison is performed by measuring a distance between probabilistic encoding models trained at different times. The statistical significance of the distance measurements are justified with a systematic statistical hypothesis test. The experimental results demonstrate that the likelihood changes over different periods with the change being greater when more distant periods are compared