  We propose a method, based on persistent homology, to uncover topological
properties of a priori unknown covariates of neuron activity. Our input data
consist of spike train measurements of a set of neurons of interest, a
candidate list of the known stimuli that govern neuron activity, and the
corresponding state of the animal throughout the experiment performed. Using a
generalized linear model for neuron activity and simple assumptions on the
effects of the external stimuli, we infer away any contribution to the observed
spike trains by the candidate stimuli. Persistent homology then reveals useful
information about any further, unknown, covariates.
