A Modular Implementation to Handle and Benchmark Drift Correction for High-Density Extracellular Recordings.

High-density neural devices are now offering the possibility to record from neuronal populations in vivo at unprecedented scale. However, the mechanical drifts often observed in these recordings are currently a major issue for "spike sorting," an essential analysis step to identify the activity of single neurons from extracellular signals. Although several strategies have been proposed to compensate for such drifts, the lack of proper benchmarks makes it hard to assess the quality and effectiveness of motion correction. In this paper, we present a benchmark study to precisely and quantitatively evaluate the performance of several state-of-the-art motion correction algorithms introduced in the literature. Using simulated recordings with induced drifts, we dissect the origins of the errors performed while applying a motion correction algorithm as a preprocessing step in the spike sorting pipeline. We show how important it is to properly estimate the positions of the neurons from extracellular traces in order to correctly estimate the probe motion, compare several interpolation procedures, and highlight what are the current limits for motion correction approaches.

Compression strategies for large-scale electrophysiology data.

Objective.With the rapid adoption of high-density electrode arrays for recording neural activity, electrophysiology data volumes within labs and across the field are growing at unprecedented rates. For example, a one-hour recording with a 384-channel Neuropixels probe generates over 80 GB of raw data. These large data volumes carry a high cost, especially if researchers plan to store and analyze their data in the cloud. Thus, there is a pressing need for strategies that can reduce the data footprint of each experiment.Approach.Here, we establish a set of benchmarks for comparing the performance of various compression algorithms on experimental and simulated recordings from Neuropixels 1.0 (NP1) and 2.0 (NP2) probes.Main results.For lossless compression, audio codecs (FLACandWavPack) achieve compression ratios (CRs) 6% higher for NP1 and 10% higher for NP2 than the best general-purpose codecs, at the expense of decompression speed. For lossy compression, theWavPackalgorithm in 'hybrid mode' increases the CR from 3.59 to 7.08 for NP1 and from 2.27 to 7.04 for NP2 (compressed file size of ∼14% for both types of probes), without adverse effects on spike sorting accuracy or spike waveforms.Significance.Along with the tools we have developed to make compression easier to deploy, these results should encourage all electrophysiologists to apply compression as part of their standard analysis workflows.

Tracking axon initial segment plasticity using high-density microelectrode arrays: A computational study.

Despite being composed of highly plastic neurons with extensive positive feedback, the nervous system maintains stable overall function. To keep activity within bounds, it relies on a set of negative feedback mechanisms that can induce stabilizing adjustments and that are collectively termed "homeostatic plasticity." Recently, a highly excitable microdomain, located at the proximal end of the axon-the axon initial segment (AIS)-was found to exhibit structural modifications in response to activity perturbations. Though AIS plasticity appears to serve a homeostatic purpose, many aspects governing its expression and its functional role in regulating neuronal excitability remain elusive. A central challenge in studying the phenomenon is the rich heterogeneity of its expression (distal/proximal relocation, shortening, lengthening) and the variability of its functional role. A potential solution is to track AISs of a large number of neurons over time and attempt to induce structural plasticity in them. To this end, a promising approach is to use extracellular electrophysiological readouts to track a large number of neurons at high spatiotemporal resolution by means of high-density microelectrode arrays (HD-MEAs). However, an analysis framework that reliably identifies specific activity signatures that uniquely map on to underlying microstructural changes is missing. In this study, we assessed the feasibility of such a task and used the distal relocation of the AIS as an exemplary problem. We used sophisticated computational models to systematically explore the relationship between incremental changes in AIS positions and the specific consequences observed in simulated extracellular field potentials. An ensemble of feature changes in the extracellular fields that reliably characterize AIS plasticity was identified. We trained models that could detect these signatures with remarkable accuracy. Based on these findings, we propose a hybrid analysis framework that could potentially enable high-throughput experimental studies of activity-dependent AIS plasticity using HD-MEAs.

How Do Spike Collisions Affect Spike Sorting Performance?

Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.

Functional neuronal circuitry and oscillatory dynamics in human brain organoids.

Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton of significantly fewer strong connections. A benzodiazepine increased the uniformity of firing patterns and decreased the relative fraction of weakly connected edges. Our analysis of the local field potential demonstrate that brain organoids contain neuronal assemblies of sufficient size and functional connectivity to co-activate and generate field potentials from their collective transmembrane currents that phase-lock to spiking activity. These results point to the potential of brain organoids for the study of neuropsychiatric diseases, drug action, and the effects of external stimuli upon neuronal networks.

ProbeInterface: A Unified Framework for Probe Handling in Extracellular Electrophysiology.

Recording neuronal activity with penetrating extracellular multi-channel electrode arrays, more commonly known as neural probes, is one of the most widespread approaches to probe neuronal activity. Despite a plethora of available extracellular probe designs, the time-consuming process of mapping of electrode channel order and relative geometries, as required by spike-sorting software is invariably left to the end-user. Consequently, this manual process is prone to mis-mapping mistakes, which in turn lead to undesirable spike-sorting errors and inefficiencies. Here, we introduce ProbeInterface, an open-source project that aims to unify neural probe metadata descriptions by removing the manual step of probe mapping prior to spike-sorting for the analysis of extracellular neural recordings. ProbeInterface is first of all a Python API, which enables users to create and visualize probes and probe groups at any required complexity level. Second, ProbeInterface facilitates the generation of comprehensive wiring description in a reproducible fashion for any specific data-acquisition setup, which usually involves the use of a recording probe, a headstage, adapters, and an acquisition system. Third, we collaborate with probe manufacturers to compile an open library of available probes, which can be downloaded at run time using our Python API. Finally, with ProbeInterface we define a file format for probe handling which includes all necessary information for a FAIR probe description and is compatible with and complementary to other open standards in neuroscience.

A modulated template-matching approach to improve spike sorting of bursting neurons.

In extracellular neural electrophysiology, individual spikes have to be assigned to their cell of origin in a procedure called "spike sorting". Spike sorting is an unsupervised problem, since no ground-truth information is generally available. Here, we focus on improving spike sorting performance, particularly during periods of high synchronous activity or so-called "bursting". Bursting entails systematic changes in spike shapes and amplitudes and remains a challenge for current spike sorting schemes. We use realistic simulated bursting recordings of high-density micro-electrode arrays (HD-MEAs) and we present a fully automated algorithm based on template matching with a focus on recovering missed spikes during bursts. To compare and benchmark spike-sorting performance after applying our method, we used ground-truth information of simulated recordings. We show that our approach can be effective in improving spike sorting performance during bursting. Further validation with experimental recordings is necessary.

SpikeInterface, a unified framework for spike sorting.

Much development has been directed toward improving the performance and automation of spike sorting. This continuous development, while essential, has contributed to an over-saturation of new, incompatible tools that hinders rigorous benchmarking and complicates reproducible analysis. To address these limitations, we developed SpikeInterface, a Python framework designed to unify preexisting spike sorting technologies into a single codebase and to facilitate straightforward comparison and adoption of different approaches. With a few lines of code, researchers can reproducibly run, compare, and benchmark most modern spike sorting algorithms; pre-process, post-process, and visualize extracellular datasets; validate, curate, and export sorting outputs; and more. In this paper, we provide an overview of SpikeInterface and, with applications to real and simulated datasets, demonstrate how it can be utilized to reduce the burden of manual curation and to more comprehensively benchmark automated spike sorters.

A Deep Learning Approach for the Classification of Neuronal Cell Types.

Classification of neurons from extracellular recordings is mainly limited to putatively excitatory or inhibitory units based on the spike shape and firing patterns. Narrow waveforms are considered to be fast spiking inhibitory neurons and broad waveforms excitatory neurons. The aim of this work is twofold. First, we intend to use the rich spatial information from high-density Multi-Electrode Arrays (MEAs) to make classification more robust; second, we hope to be able to classify sub-types of excitatory and inhibitory neurons. We first built, in simulation, a large dataset of action potentials from detailed neural models. Then, we extracted spike features from the simulated recordings on a high-density Multi-Electrode Array model. Finally, we used a Convolutional Neural Networks (CNN), to classify the different cell types. Compared with the ground truth data from the simulated dataset, the results show that this forward modelling/machine learning approach is very robust in recognizing excitatory and inhibitory spikes (accuracy $\ge 92.15$%). Additionally, the approach can, to a certain extent, correctly classify different cell sub-types. As the detail and fidelity of neural models increase and high-density recordings become available, this approach could become a viable and robust alternative for classification of neural cell types from in-vivo extracellular recordings.

Independent Component Analysis for Fully Automated Multi-Electrode Array Spike Sorting.

In neural electrophysiology, spike sorting allows to separate different neurons from extracellularly measured recordings. It is an essential processing step in order to understand neural activity and it is an unsupervised problem in nature, since no ground truth information is available. There are several available spike sorting packages, but many of them require a manual intervention to curate the results, which makes the process time consuming and hard to reproduce. Here, we focus on high-density Multi-Electrode Array (MEA) recordings and we present a fully automated pipeline based on Independent Component Analysis (ICA). While ICA has been previously investigated for spike sorting, it has never been compared with fully automated state-of-the-art algorithms. We use realistic simulated datasets to compare the spike sorting performance in terms of complexity, signal-to-noise ratio, and recording duration. We show that an ICA-based fully automated spike sorting approach can be a viable alternative approach due to its precision and robustness, but it needs to be optimized for time constraints and requires sufficient density of electrodes to cover active neurons in the proximity of the MEA.

Combining biophysical modeling and deep learning for multielectrode array neuron localization and classification.

Neural circuits typically consist of many different types of neurons, and one faces a challenge in disentangling their individual contributions in measured neural activity. Classification of cells into inhibitory and excitatory neurons and localization of neurons on the basis of extracellular recordings are frequently employed procedures. Current approaches, however, need a lot of human intervention, which makes them slow, biased, and unreliable. In light of recent advances in deep learning techniques and exploiting the availability of neuron models with quasi-realistic three-dimensional morphology and physiological properties, we present a framework for automatized and objective classification and localization of cells based on the spatiotemporal profiles of the extracellular action potentials recorded by multielectrode arrays. We train convolutional neural networks on simulated signals from a large set of cell models and show that our framework can predict the position of neurons with high accuracy, more precisely than current state-of-the-art methods. Our method is also able to classify whether a neuron is excitatory or inhibitory with very high accuracy, substantially improving on commonly used clustering techniques. Furthermore, our new method seems to have the potential to separate certain subtypes of excitatory and inhibitory neurons. The possibility of automatically localizing and classifying all neurons recorded with large high-density extracellular electrodes contributes to a more accurate and more reliable mapping of neural circuits. NEW & NOTEWORTHY We propose a novel approach to localize and classify neurons from their extracellularly recorded action potentials with a combination of biophysically detailed neuron models and deep learning techniques. Applied to simulated data, this new combination of forward modeling and machine learning yields higher performance compared with state-of-the-art localization and classification methods.

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

With the latest development in the design and fabrication of high-density Multi-Electrode Arrays (MEA) for in-vivo neural recordings, the spatiotemporal information in the recorded signals allows for refined estimation of a neuron's location around the probe. In parallel, advances in computational models for neural activity enables simulation of recordings from neurons with detailed morphology. Our approach uses deep learning algorithms on a large set of such simulation data to extract the 3D position of the neuronal somata. Multi-compartment models from 13 different neural morphologies in layer 5 (L5) of the rat's neocortex are placed at random locations and with different alignments with respect to the MEA. The sodium trough and repolarisation peak images on the MEA serve as input features for a Convolutional Neural Network (CNN), which predicts the neural location robustly and with low error rates. The forward modeling/machine learning approach yields very accurate results for the different morphologies and is able to cope with different neuron alignments.