Scalable Spike Source 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 spike9s source location, a powerful feature for determining the firing neuron9s position and identity in the recording. Here we present a novel, generative model for inferring the source of individual spikes given observed electrical traces. To allow for scalable, efficient inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on biophysically realistic simulated datasets, showing that our method outperforms heuristic localization methods such as center of mass and can improve spike sorting performance significantly. We further apply our model to real data to show that it is an effective, interpretable tool for analyzing largescale extracellular recordings.

Detecting NonRedundant Collective Activity of Neurons Brain activity is characterized by the activity of huge number of neurons that are densely connected in complex networks. Detecting how neural activity is coordinated across time and space over brain circuits is a very challenging task. In particular, ensembles of neurons may coactivate in specific patterns whose expression correlates to various stimuli, cognitive states, or behavioral outcomes. These patterns are expressed on a variety of timescales, from milliseconds to hundreds of milliseconds or seconds. Here, we extend a recentlyintroduced method for extracting stereotypical firing patterns by applying several postprocessing steps that enable the precise estimation of pattern wavefronts irrespective of the integration timescale used to detect patterns. Using electrophysiology data recorded in mouse visual cortex, we show that this approach enables a more precise estimation of the relation between firing patterns and mesoscale dynamics, reflected in the localfield potential. The method removes the redundancy and blurring generated by the convolution of spiketrains with exponentiallydecaying kernels and offers a sharper representation of the expression of firing patterns in brain data.