Learning multiscale neural metrics via entropy minimization

In order to judiciously compare neural responses between repeated trials or stimuli, a well-suited distance metric is necessary. With multi-electrode recordings, a neural response is a spatiotemporal pattern, but not all of the dimensions of space and time should be treated equally. In order to understand which dimensions of the input are more discriminative and to improve the classification performance, we propose a metric-learning approach that can be used across scales. This extends previous work that used a linear projection into lower dimensional space; here, multiscale metrics or kernels are learned as the weighted combinations of different metrics or kernels on each of the neural response´s dimensions. Preliminary results are explored on a cortical recording of a rat during a tactile stimulation experiment. Metrics on both local field potential and spiking data are explored. The learned weights reveal important dimensions of the response, and the learned metrics improve nearest-neighbor classification performance.