scholarly journals Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

2016 ◽  
Author(s):  
Marius Pachitariu ◽  
Nicholas Steinmetz ◽  
Shabnam Kadir ◽  
Matteo Carandini ◽  
Harris Kenneth D.
Keyword(s):  
Large Scale ◽  
Superior Performance ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Manual Curation ◽  
Electrophysiological Recordings ◽  
Dimensional Approximation ◽  
Low Dimensional ◽  
Channel Electrode

AbstractAdvances in silicon probe technology mean that in vivo electrophysiological recordings from hundreds of channels will soon become commonplace. To interpret these recordings we need fast, scalable and accurate methods for spike sorting, whose output requires minimal time for manual curation. Here we introduce Kilosort, a spike sorting framework that meets these criteria, and show that it allows rapid and accurate sorting of large-scale in vivo data. Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved. Rapid processing is achieved thanks to a novel low-dimensional approximation for the spatiotemporal distribution of each template, and to batch-based optimization on GPUs. A novel post-clustering merging step based on the continuity of the templates substantially reduces the requirement for subsequent manual curation operations. We compare Kilosort to an established algorithm on data obtained from 384-channel electrodes, and show superior performance, at much reduced processing times. Data from 384-channel electrode arrays can be processed in approximately realtime. Kilosort is an important step towards fully automated spike sorting of multichannel electrode recordings, and is freely available (github.com/cortex-lab/Kilosort).

scholarly journals Fast and accurate spike sorting in vitro and in vivo for up to thousands of electrodes

2016 ◽  
Author(s):  
Pierre Yger ◽  
Giulia L.B. Spampinato ◽  
Elric Esposito ◽  
Baptiste Lefebvre ◽  
Stéphane Deny ◽  
...  
Keyword(s):  
Template Matching ◽  
Large Scale ◽  
Ground Truth ◽  
Spike Sorting ◽  
Electrode Arrays ◽  
Ground Truth Data ◽  
Sorting Problem ◽  
Large Populations

AbstractUnderstanding how assemblies of neurons encode information requires recording large populations of cells in the brain. In recent years, multi-electrode arrays and large silicon probes have been developed to record simultaneously from hundreds or thousands of electrodes packed with a high density. However, these new devices challenge the classical way to do spike sorting. Here we developed a new method to solve these issues, based on a highly automated algorithm to extract spikes from extracellular data, and show that this algorithm reached near optimal performance both in vitro and in vivo. The algorithm is composed of two main steps: 1) a “template-finding” phase to extract the cell templates, i.e. the pattern of activity evoked over many electrodes when one neuron fires an action potential; 2) a “template-matching” phase where the templates were matched to the raw data to find the location of the spikes. The manual intervention by the user was reduced to the minimal, and the time spent on manual curation did not scale with the number of electrodes. We tested our algorithm with large-scale data from in vitro and in vivo recordings, from 32 to 4225 electrodes. We performed simultaneous extracellular and patch recordings to obtain “ground truth” data, i.e. cases where the solution to the sorting problem is at least partially known. The performance of our algorithm was always close to the best expected performance. We thus provide a general solution to sort spikes from large-scale extracellular recordings.

scholarly journals YASS: Yet Another Spike Sorter

2017 ◽  
Author(s):  
JinHyung Lee ◽  
David Carlson ◽  
Hooshmand Shokri ◽  
Weichi Yao ◽  
Georges Goetz ◽  
...  
Keyword(s):  
Dirichlet Process ◽  
Large Scale ◽  
Matching Pursuit ◽  
Spike Sorting ◽  
Dirichlet Process Mixture ◽  
Electrode Arrays ◽  
Neural Signals ◽  
Model Framework ◽  
Multi Stage ◽  
Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

scholarly journals A Bayesian Dynamical Approach for Human Action Recognition

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5613
Author(s):  
Amirreza Farnoosh ◽  
Zhouping Wang ◽  
Shaotong Zhu ◽  
Sarah Ostadabbas
Keyword(s):  
Action Recognition ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Human Action Recognition ◽  
Human Action ◽  
Superior Performance ◽  
Action Classification ◽  
Motion Data ◽  
Highly Correlated ◽  
Low Dimensional

We introduce a generative Bayesian switching dynamical model for action recognition in 3D skeletal data. Our model encodes highly correlated skeletal data into a few sets of low-dimensional switching temporal processes and from there decodes to the motion data and their associated action labels. We parameterize these temporal processes with regard to a switching deep autoregressive prior to accommodate both multimodal and higher-order nonlinear inter-dependencies. This results in a dynamical deep generative latent model that parses meaningful intrinsic states in skeletal dynamics and enables action recognition. These sequences of states provide visual and quantitative interpretations about motion primitives that gave rise to each action class, which have not been explored previously. In contrast to previous works, which often overlook temporal dynamics, our method explicitly model temporal transitions and is generative. Our experiments on two large-scale 3D skeletal datasets substantiate the superior performance of our model in comparison with the state-of-the-art methods. Specifically, our method achieved 6.3% higher action classification accuracy (by incorporating a dynamical generative framework), and 3.5% better predictive error (by employing a nonlinear second-order dynamical transition model) when compared with the best-performing competitors.

Polytrodes: High-Density Silicon Electrode Arrays for Large-Scale Multiunit Recording

2005 ◽  
Vol 93 (5) ◽  
pp. 2987-3000 ◽  
Author(s):  
Timothy J. Blanche ◽  
Martin A. Spacek ◽  
Jamille F. Hetke ◽  
Nicholas V. Swindale
Keyword(s):  
Large Scale ◽  
High Density ◽  
Clustering Methods ◽  
Electrode Arrays ◽  
Large Numbers ◽  
High Bandwidth ◽  
Electrode Positioning ◽  
Multiunit Recording ◽  
Silicon Based

We developed a variety of 54-channel high-density silicon electrode arrays (polytrodes) designed to record from large numbers of neurons spanning millimeters of brain. In cat visual cortex, it was possible to make simultaneous recordings from >100 well-isolated neurons. Using standard clustering methods, polytrodes provide a quality of single-unit isolation that surpasses that attainable with tetrodes. Guidelines for successful in vivo recording and precise electrode positioning are described. We also describe a high-bandwidth continuous data-acquisition system designed specifically for polytrodes and an automated impedance meter for testing polytrode site integrity. Despite having smaller interconnect pitches than earlier silicon-based electrodes of this type, these polytrodes have negligible channel crosstalk, comparable reliability, and low site impedances and are capable of making high-fidelity multiunit recordings with minimal tissue damage. The relatively benign nature of planar electrode arrays is evident both histologically and in experiments where the polytrode was repeatedly advanced and retracted hundreds of microns over periods of many hours. It was possible to maintain stable recordings from active neurons adjacent to the polytrode without change in their absolute positions, neurophysiological or receptive field properties.

scholarly journals What Happens with the Circuit in Alzheimer's Disease in Mice and Humans?

2018 ◽  
Vol 41 (1) ◽  
pp. 277-297 ◽  
Author(s):  
Benedikt Zott ◽  
Marc Aurel Busche ◽  
Reisa A. Sperling ◽  
Arthur Konnerth
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Neuronal Damage ◽  
Strong Support ◽  
Underlying Disease ◽  
Electrophysiological Recordings

A major mystery of many types of neurological and psychiatric disorders, such as Alzheimer's disease (AD), remains the underlying, disease-specific neuronal damage. Because of the strong interconnectivity of neurons in the brain, neuronal dysfunction necessarily disrupts neuronal circuits. In this article, we review evidence for the disruption of large-scale networks from imaging studies of humans and relate it to studies of cellular dysfunction in mouse models of AD. The emerging picture is that some forms of early network dysfunctions can be explained by excessively increased levels of neuronal activity. The notion of such neuronal hyperactivity receives strong support from in vivo and in vitro cellular imaging and electrophysiological recordings in the mouse, which provide mechanistic insights underlying the change in neuronal excitability. Overall, some key aspects of AD-related neuronal dysfunctions in humans and mice are strikingly similar and support the continuation of such a translational strategy.

scholarly journals Polymer skulls with integrated transparent electrode arrays for cortex-wide opto-electrophysiological recordings

2021 ◽  
Author(s):  
Preston D Donaldson ◽  
Zahra S Navabi ◽  
Russell E Carter ◽  
Skylar M. L. Fausner ◽  
Leila Ghanbari ◽  
...  
Keyword(s):  
Optical Imaging ◽  
Large Scale ◽  
High Spatial Resolution ◽  
Transparent Electrode ◽  
Brain Regions ◽  
Electrode Arrays ◽  
Sensory Stimuli ◽  
Cortical Dynamics ◽  
Multimodal Sensing ◽  
Electrophysiological Recordings

Electrophysiology and optical imaging provide complementary neural sensing capabilities; electrophysiological recordings have the highest temporal resolution, while optical imaging allows recording the activities of genetically defined populations at high spatial resolution. Combining these complementary, yet orthogonal modalities to perform simultaneous large-scale, multimodal sensing of neural activity across multiple brain regions would be very powerful. Here we show that transparent, inkjet-printed electrocorticography (ECoG) electrode arrays can be seamlessly integrated with morphologically conformant transparent polymer skulls for multimodal recordings across the cortex. These eSee-Shells, were implanted on transgenic mice expressing the Ca2+ indicator GCaMP6f in cortical excitatory cells and provided a robust opto-electrophysiological interface for over 100 days. eSee-Shells enable simultaneous mesoscale Ca2+ imaging and ECoG acquisition under anesthesia as well as in awake animals presented with sensory stimuli. eSee-Shells further show sufficient clarity and transparency to observe single-cell Ca2+ signals directly below the electrodes and interconnects. Simultaneous multimodal measurement of cortical dynamics reveals changes in both ECoG and Ca2+ signals that depend on the behavioral state.

scholarly journals Neurophysiology of the Developing Cerebral Cortex: What We Have Learned and What We Need to Know

2022 ◽  
Vol 15 ◽  
Author(s):  
Heiko J. Luhmann
Keyword(s):  
Cerebral Cortex ◽  
Large Scale ◽  
Activity Patterns ◽  
Neuronal Cell ◽  
Electrode Arrays ◽  
Novel Technologies ◽  
The Past ◽  
Sensory Evoked

This review article aims to give a brief summary on the novel technologies, the challenges, our current understanding, and the open questions in the field of the neurophysiology of the developing cerebral cortex in rodents. In the past, in vitro electrophysiological and calcium imaging studies on single neurons provided important insights into the function of cellular and subcellular mechanism during early postnatal development. In the past decade, neuronal activity in large cortical networks was recorded in pre- and neonatal rodents in vivo by the use of novel high-density multi-electrode arrays and genetically encoded calcium indicators. These studies demonstrated a surprisingly rich repertoire of spontaneous cortical and subcortical activity patterns, which are currently not completely understood in their functional roles in early development and their impact on cortical maturation. Technological progress in targeted genetic manipulations, optogenetics, and chemogenetics now allow the experimental manipulation of specific neuronal cell types to elucidate the function of early (transient) cortical circuits and their role in the generation of spontaneous and sensory evoked cortical activity patterns. Large-scale interactions between different cortical areas and subcortical regions, characterization of developmental shifts from synchronized to desynchronized activity patterns, identification of transient circuits and hub neurons, role of electrical activity in the control of glial cell differentiation and function are future key tasks to gain further insights into the neurophysiology of the developing cerebral cortex.

scholarly journals High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays

2018 ◽  
Author(s):  
Jason E. Chung ◽  
Hannah R. Joo ◽  
Jiang Lan Fan ◽  
Daniel F. Liu ◽  
Alex H. Barnett ◽  
...  
Keyword(s):  
Large Scale ◽  
Brain Activity ◽  
Brain Regions ◽  
Electrode Arrays ◽  
Long Term Stability ◽  
Electrophysiological Recordings ◽  
Spatial Coverage ◽  
Recording Channels ◽  
Distributed Circuits ◽  
Polymer Electrodes

AbstractThe brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

scholarly journals Feedback inhibition and its control in an insect olfactory circuit

2019 ◽  
Author(s):  
Subhasis Ray ◽  
Zane N. Aldworth ◽  
Mark A. Stopfer
Keyword(s):  
Large Scale ◽  
Dynamic Range ◽  
Feedback Inhibition ◽  
Inhibitory Neurons ◽  
Biophysical Model ◽  
Intrinsic Properties ◽  
Olfactory Pathway ◽  
Olfactory Responses ◽  
Electrophysiological Recordings

AbstractInhibitory neurons play critical roles in regulating and shaping olfactory responses in vertebrates and invertebrates. In insects, these roles are performed by relatively few neurons, which can be interrogated efficiently, revealing fundamental principles of olfactory coding. Here, with electrophysiological recordings from the locust and a large-scale biophysical model, we analyzed the properties and functions of GGN, a unique giant GABAergic neuron that plays a central role in structuring olfactory codes in the locust mushroom body. Analysis of our in vivo recordings and simulations of our model of the olfactory network suggests that GGN extends the dynamic range of KCs, and leads us to predict the existence of a yet undiscovered olfactory pathway. Our analysis of GGN’s intrinsic properties, inputs, and outputs, in vivo and in silico, reveals basic new features of this critical neuron and the olfactory network that surrounds it.

scholarly journals Global organization of neuronal activity only requires unstructured local connectivity

eLife ◽  
2022 ◽  
Vol 11 ◽  
Author(s):  
David Dahmen ◽  
Moritz Layer ◽  
Lukas Deutz ◽  
Paulina Anna Dąbrowska ◽  
Nicole Voges ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Network Models ◽  
Local Connectivity ◽  
Reach To Grasp ◽  
Electrophysiological Recordings ◽  
Global Organization ◽  
Network States ◽  
Low Dimensional ◽  
Coordination Patterns

Modern electrophysiological recordings simultaneously capture single-unit spiking activities of hundreds of neurons spread across large cortical distances. Yet, this parallel activity is often confined to relatively low-dimensional manifolds. This implies strong coordination also among neurons that are most likely not even connected. Here, we combine in vivo recordings with network models and theory to characterize the nature of mesoscopic coordination patterns in macaque motor cortex and to expose their origin: We find that heterogeneity in local connectivity supports network states with complex long-range cooperation between neurons that arises from multi-synaptic, short-range connections. Our theory explains the experimentally observed spatial organization of covariances in resting state recordings as well as the behaviorally related modulation of covariance patterns during a reach-to-grasp task. The ubiquity of heterogeneity in local cortical circuits suggests that the brain uses the described mechanism to flexibly adapt neuronal coordination to momentary demands.

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