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  2. Jeremy Magland and James Jun Discuss the Flatiron Institute and Spike Sorting Algorithms

Jeremy Magland and James Jun Discuss the Flatiron Institute and Spike Sorting Algorithms

Mar 16, 2020

Jeremy Magland and James Jun are researchers at the Flatiron Institute whose work involves spike sorting for analysis of large recorded neuronal data sets. In this episode, at the SfN 2019 Conference, they discuss the Flatiron Institute, spike sorting and the various algorithms involved in it, as well as an open-source algorithm the Flatiron Institute has developed for spike sorting and how it works.

Top three takeaways:

  1. The Flatiron Institute, a division of the Simons Foundation, uses modern computing tools to advance scientific understanding, and they provide open-source code to aid labs in data analysis.
  2. Spike sorting algorithms take recorded extracellular data and use statistical methods to group it into clusters, from which it determines the number of neurons that are firing. The difference in clustering and sorting depends on the specific type of analysis the algorithm runs.
  3. MountainSort is an open-source spike sorting software that is distinct in that it doesn’t require as many input parameters as other algorithms do.

[0:40] Ladan introduces the episode and the guests, James Jun and Jeremy Magland, at SfN 2019; Jun gives his background and what he is studying

[3:40] Jun explains how spike sorting uses extracellular recordings to receive signals from different neurons at once

[4:55] Magland gives his background and what he is studying

[8:05] There’s a rainbow

[9:10] Magland and Jun explain some advantages/benefits of the Flatiron Institute, a research division of the Simons Foundation; the institute creates open-source software to help labs with spike sorting

[11:40] Jun discusses the Simons Foundation, how they started, how they created the Flatiron Institute, and the types of projects they fund

[14:10] MountainSort is the open-source spike sorting algorithm developed by Flatiron which clusters spikes by using a statistical method to detect differences in spike densities and separate the neurons accordingly. This doesn’t require adjustable parameters as input, unlike other software.

[16:40] Jun discusses some differences in certain spike sorting algorithms and the type of analysis they use to sort spikes into different clusters and differentiate the neurons

[19:55] Magland and Jun are looking forward to enhanced hardware and computing capabilities that improve the speed and accuracy of spike sorting