Welcome to Spruce’s documentation!

Spruce (2018-2020) was a project by LeafLabs, LLC and the Synthetic Neurobiology Group at MIT to gather high-quality neurophysiological data, with simultaneous extracellular and intracellular recordings, and use it to provide a ground-truth benchmark for new and existing spike sorting algorithms. Details about the data collection can be found in Brian Allen’s paper, and the raw data can be downloaded from {link TBD}. This website exists to describe the benchmarking results and some of their conclusions, as well as to provide an interactive viewer for analyzing them.

The top-line results are as follows:

• MountainSort4’s performance is strongly dependent on electrode density; it requires a low density to be effective.

• Kilosort and KiloSort2 tend to have a “plateau” of performance; adding more data does not improve performance, once they have enough channels to reach their peak.

• KiloSort2 seems to be a “high-risk, high-reward” improvement of Kilosort; it performs worse on more difficult datasets, but better on higher-quality datasets.

• The traditional spike-sorting pipeline (detection, feature extraction, clustering) is not as powerful as more sophisticated approaches. We explored using deep learning to improve traditional spike-sorting performance, and it still did not beat Kilosort.

Contents:

• Spruce Overview
  • Introduction
  • Methodology
  • Results
  • Conclusions
  • Useful Links
• Data Inspector
  • Basic Usage
  • Inspector
  • Glossary
  • Suggested Usage
• Aggregate Data Viewer
• Deep Learning Supplemental
  • Introduction
  • Approach
  • Results

Indices and tables

• Index

• Search Page