Latent state visualization of neural firing rates

Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the neural states corresponding to different portions of the behavior task. A simulation using a dynamical model lends credence to the ability of the technique to generate a representation that preserves underlying dynamics of the model. This technique is a straightforward way to extract a useful visualization for neural recordings during brain-machine interface tasks. Meaningful visualization confirms underlying structure in data, which can be captured with parametric modeling.