Tracking Time Variant Neuron Tuning Properties of Brain Machine Interfaces

Tuning properties of neurons, which represent how information is encoded in neural firing, are well accepted as time variant. For a steady-performed brain machine interface (BMI), the decoding algorithm should be able to catch this change in time. Unfortunately, an assumption-less tuning property is too complicate to trace. Simplifying the tuning curve to linear or exponential one may lose important information. We propose to approximate the tuning curve with multiple Gaussian functions, and modeled the non-stationary tuning curves by the changes on the Gaussian parameters. Applied on in vivo neural data when the monkey is performing a 2-dimension tracking task, we found the non-stationary tuning properties can be tracked by the changes on parameters of Gaussian components, which greatly decreases the number of parameters need to be observed. Following this idea, we can design an adaptive method by updating parameters of tuning model.