Scalable Spike Localization in Extracellular Recordings using Amortized Variational Inference


Extracellular recordings using modern, dense probes provide detailed footprints of action potentials (spikes) from thousands of neurons simultaneously. Inferring the activity of single neurons from these recordings, however, is a complex blind source separation problem, complicated both by the high intrinsic data dimensionality and large data volume. Despite these complications, dense probes can allow for the estimation of a spike’s source location, a powerful feature for determining the firing neuron’s position and identity in the recording. At this talk, I present a novel, generative model for inferring the source of individual spikes given observed electrical traces.