“Validating silicon polytrodes with paired juxtacellular recordings: method and dataset”

Abstract

Cross-validating new methods for recording neural activity is necessary to accurately interpret and compare the signals they measure. Here we describe a procedure for precisely aligning two probes for in vivo "paired-recordings" such that the spiking activity of a single neuron is monitored with both a dense extracellular silicon polytrode and a juxtacellular micro-pipette. Our new method allows for efficient, reliable, and automated guidance of both probes to the same neural structure with micron resolution. We also describe a new dataset of paired-recordings, which is available online. We propose that our novel targeting system, and ever expanding cross-validation dataset, will be vital to the development of new algorithms for automatically detecting/sorting single-units, characterizing new electrode materials/designs, and resolving nagging questions regarding the origin and nature of extracellular neural signals.

Localizing neuronal somata from Multi-Electrode Array in-vivo recordings using deep learning

“…In this work we did not include any noise in the simulated recordings, with the rationale that sorted spikes can be cleaned by applying spike-triggered-averaging. Of course, validation on real data is required and we are working on paired approaches with either electrophysiological recordings only [12], or involving also calcium/voltage imaging.…”

Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction

“…In addition to the external noise sources, neural recordings contain background spiking activities from distant sources that are too weak to resolve but still contribute to noise. Spikes from distant neurons are added to a broad range of sites and they corrupt spike waveforms from nearby sources (Neto et al 2016) . Spatially varying noise such as the background spikes and the motion artifact can be cancelled by applying a local common average referencing (L-CAR) scheme ( Figure B).…”

“…We also included in-vivo juxtacellular recordings generated by Neto et al (Neto et al 2016) . They used a similar methodology to generate simultaneous juxta-and extracellular recordings in the motor cortex of an anesthetized rat.…”

“…First, we used simultaneous intracellular and extracellular recordings from same neurons in slice preparations ( [10,18] ) and in vivo ( [10,18] ; Figure 4A). Although this approach (paired recording) is limited to one known neuron per recording session, it provides precise timing of all extracellular spikes emitted by a single neuron recorded via intracellular whole-cell or juxtacellular patching.…”

“…The global probe movement is automatically detected and corrected using the spatial correlation of spike positions across time. We evaluated the accuracy of JRCLUST using three types of ground-truth datasets specific to HD probes obtained from manual curation (Pachitariu et al 2016) , paired juxtacellular recordings (Neto et al 2016) and biophysical simulations (Hawrylycz et al 2016) .…”