Recording from the same neuron with high-density CMOS probes and patch-clamp: a ground-truth dataset and an experiment in collaboration

Abstract

We built a rig to perform patch-clamp and extracellular recordings from the same neuron in vivo. In this setup, the axes of two micromanipulators are precisely aligned and their relative position tracked in real-time, allowing us to accurately target patch-clamp recordings to neurons near an extracellular probe. We used this setup to generate a publicly-available dataset where a cortical neuron's spiking activity is recorded in patch-clamp next to a dense CMOS Neuropixels probe. "Ground-truth" datasets of this kind are rare but valuable to the neuroscience community, as they power the development and improvement of spike-sorting and analysis algorithms, tethering them to empirical observations. In this article, we describe our approach and report exploratory and descriptive analysis on the resulting dataset. We study the detectability of patch-clamp spikes on the extracellular probe, within-unit reliability of spike features and spatiotemporal dynamics of the action potential waveform. We open discussion and collaboration on this dataset through an online repository, with a view to producing follow-up publications. PrologueOur efforts to record from the same neuron in vivo using patch-clamp and dense extracellular probes have resulted in three outputs: a publicly-available dataset (http://bit.ly/paired_recs), a manuscript, and a code repository (http://bit.ly/paired_git). Together, these three components form the publication arising from the experiments we have performed. The role of the dataset is to be downloaded and re-used. The role of the manuscript is to describe the experimental methods through which we acquired the dataset, explain it and showcase which types of questions it can be used to address. The repository has two roles: first, promoting reproducibility and error correction.By making our analysis and figure-generation code freely-available, we wish to make our analysis procedures clear and enable the reader to reproduce our results from the raw data, alerting us to any potential mistakes. Second, the repository will form a living, dynamic and interactive component of the publication: a forum for open collaboration on this dataset. Any interested scientists can contribute to it, joining us in detailed exploration of these recordings with a view to producing follow-up publications in which they will be credited for their input.Why did we opt to publish this way? The first reason is that the very nature of the project we here describe -recording the same neuron with patch-clamp and extracellular probes -invites an open science and open source approach. This is because the primary use of this type of "ground truth" validation data is to aid the development of new sorting and analysis algorithms, as well as to benchmark and improve existing ones. The second reason is that despite being conceptually very simple, this project generated a large and complex dataset that can be tackled in many ways and used to address different types of question. Some of these questions are beyond the reach of our analytical...

Reconstruction of in-vivo subthreshold activity of single neurons from large-scale spiking recordings

“…Here we apply the algorithm for reconstruction of membrane potential on a dataset of extracellular activity recorded with the Neuropixels probe (Jun et al 2017) from the somatosensory cortex of the rat that was acquired in the Kampff lab (Marques-Smith et al 2018a, Marques-Smith et al 2018b. Depending on the session, between 50 and 200 units were identified using the Kilo-sort spike sorting algorithm (Pachitariu et al 2016).…”

MEArec: a fast and customizable testbench simulator for ground-truth extracellular spiking activity

“…Alternatively, other spike sorters are validated on experimental paired ground-truth recordings [9,47]. While these valuable datasets [20,22,37,33] can certainly provide useful information, the low count of ground-truth units makes the validation incomplete and could result in biases (for example algorithm-specific parameters could be tuned to reach a higher performance for the recorded ground-truth units). A third validation method consist of using simulated ground-truth recordings [12].…”

“…Spike sorting is unsupervised by definition, as the recorded signals are only measured extracellularly with no knowledge of the underlying spiking activity. A few attempts to provide ground-truth datasets, for example by combining extracellular and patch-clamp or juxtacellular recordings [22,20,37,47,33,1] exist, but the main limitation of this approach is that only one or a few cells can be patched at the same time, providing very limited ground-truth information with respect to the number of neurons that can be recorded simultaneously from extracellular probes. An alternative method consists of adding artificial or previously-sorted and well-isolated spikes in the recordings (hybrid method) [44,46].…”

Recording site placement on planar silicon-based probes affects neural signal quality: edge sites enhance acute recording performance

“…The fourth and fifth devices are two variants of the recently developed Neuropixels CMOS-based silicon probe ( [3,50]; www.neuropixels.org; figure 1(d) and (e)). The commercially available version has a 10 mm long shank and a cross-section of 70 μm × 20 μm (W × T; figure 1(d)).…”