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About me





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.

SpikeInterface: An open-source framework for sorting, analysis, and evaluation of extracellular recordings


Recent breakthroughs in microelectronics have enabled high precision extracellular recording of thousands of neurons both in vitro and in vivo. While the increased data volume and complexity offers unprecedented opportunities for understanding brain function, it also heightens the need for standardized, reproducible analysis techniques. To this end, we developed SpikeInterface, a framework for extracting and analyzing relevant information from both raw and spike-sorted extracellular datasets of any established file format. SpikeInterface was designed to standardize how data is retrieved from files, rather than how it is stored, allowing users to access, sort, and analyze extracellular datasets with the same tools, regardless of the underlying file format. With this framework, we hope to facilitate standardized analysis and visualization of extracellular data, promote straightforward reuse of extracellular datasets, increase the reproducibility of electrophysiological studies using spike sorting software, and address issues of file format compatibility within electrophysiology research without creating yet another file format.

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference


Determining the positions of neurons in an extracellular recording is useful for investigating the functional properties of the underlying neural circuitry. In this work, we present a Bayesian modelling approach for localizing the source of individual spikes on high-density, microelectrode arrays. To allow for scalable inference, we implement our model as a variational autoencoder and perform amortized variational inference. We evaluate our method on both biophysically realistic simulated and real extracellular datasets, demonstrating that it is more accurate than and can improve spike sorting performance over heuristic localization methods such as center of mass.

SpikeInterface, a unified framework for spike sorting


Given the importance of understanding single-neuron activity, much development has been directed towards improving the performance and automation of spike sorting. These developments, however, introduce new challenges, such as file format incompatibility and reduced interoperability, that hinder benchmarking and preclude reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to standardize extracellular data file operations. With a few lines of code and regardless of the underlying data format, researchers can: run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to both real and simulated extracellular datasets, demonstrate how it can improve the accessibility, reliability, and reproducibility of spike sorting in preparation for the widespread use of large-scale extracellular electrophysiology.


Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.