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Massachusetts Institute of Technology

Ground truth in ultra-dense neural recording

Abstract

dc:description.abstract

While biologists routinely record neural activity with multi-electrodes, spike sorting-- the process of attributing spikes to particular neurons-- remains a challenge that typically requires human curation. Due to technical limitations, there have been very few multi-electrode recordings done in concert with techniques such as patch clamp, which report the "ground truth" voltage state of a single neuron in a population. Such recordings would allow for the direct evaluation of spike sorting, which in turn could lead to further development and refinement of spike sorting methods. We developed a technique to establish a whole-cell or cell-attached patch recording in a cortical neuron of an awake or lightly anesthetized head-fixed mouse, with simultaneous extracellular recording of the same neuron and its neighbors with arrays of close-packed, "ultra-dense," electrodes (64-256, 9 x 9[mu]m electrodes spaced 2[mu]m apart on a shank). Our recordings constitute ground truth for spike sorting evaluation, and allow for the direct evaluation and improvement of an algorithm for automatic spike sorting that benefits from high electrode density relative to neuron packing density. Using this technique we show the patch-triggered extracellular waveforms of neurons at a high level of granularity distributed across cortex, and give a glimpse into the spiking activity of the network surrounding a patched neuron in vivo. We explore the dataset generated with this technique and discover a spike-bursting trajectory exhibiting apparent spike-frequency adaptation. This bursting trajectory was readily apparent in deep but not shallow cortical neurons in patch recordings, but was somewhat obscured in extracellular recordings, where spikes from neighboring neurons may overlap in time to contribute "noise." We show how this trajectory can be easily seen in a high-amplitude extracellular recording, and propose how it may be accentuated in lower amplitude recording through the use of blind source separation.

Degree

thesis:*
Department dc:contributor.department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2017

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Allen, Brian D. (Brian Douglas)
Advisor dc:contributor.advisor
  • Edward S. Boyden, Ill.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/109655
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/109655

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Allen, Brian D. (Brian Douglas). Ground truth in ultra-dense neural recording. Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/109655