scholarly journals Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

2021 ◽  
Author(s):  
Nathan J Hall ◽  
David J Herzfeld ◽  
Stephen G Lisberger
Keyword(s):  
Failure Modes ◽  
Ground Truth ◽  
Spike Sorting ◽  
Appropriate Use ◽  
Neural Data ◽  
Sorting Algorithms ◽  
Recording Session ◽  
Neural Spike ◽  
Detailed Assessment ◽  
Process Speed

We evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called "full binary pursuit" or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally assign all the spike events in the original voltages to separable neurons. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on ground-truth datasets reveals many of the failure modes of spike sorting. We examine overall spike sorter performance in ground-truth datasets and suggest post-sorting analyses that can improve the veracity of neural analyses by minimizing the intrusion of failure modes into analysis and interpretation of neural data. Our analysis reveals the tradeoff between the number of channels a sorter can process, speed of sorting, and some of the failure modes of spike sorting. FBP works best on data from 32 channels or fewer. It trades speed and number of channels for avoidance of specific failure modes that would be challenges for some use cases. We conclude that all spike sorting algorithms studied have advantages and shortcomings, and the appropriate use of a spike sorter requires a detailed assessment of the data being sorted and the experimental goals for analyses.

scholarly journals Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

2021 ◽  
Author(s):  
Nathan J. Hall ◽  
David J. Herzfeld ◽  
Stephen G. Lisberger
Keyword(s):  
Failure Modes ◽  
Ground Truth ◽  
Spike Sorting ◽  
Neural Data ◽  
Neural Recordings ◽  
Recording Session ◽  
Neural Spike

AbstractWe evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called “full binary pursuit” or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally recognize the spike events in the original voltages. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on real and simulated ground-truth datasets reveals many of the failure modes of spike sorters. We suggest a set of post-sorting analyses that can improve the veracity of neural recordings by minimizing the intrusion of those failure modes into analysis and interpretation of neural data.

scholarly journals Validation of neural spike sorting algorithms without ground-truth information

2016 ◽  
Vol 264 ◽  
pp. 65-77 ◽  
Author(s):  
Alex H. Barnett ◽  
Jeremy F. Magland ◽  
Leslie F. Greengard
Keyword(s):  
Ground Truth ◽  
Spike Sorting ◽  
Sorting Algorithms ◽  
Neural Spike ◽  
Ground Truth Information

An Investigation on Neural Spike Sorting Algorithms

2021 ◽  
Author(s):  
Helat A. Hussein ◽  
Subhi R. M. Zeebaree ◽  
Mohammed A. M. Sadeeq ◽  
Hanan M. Shukur ◽  
Ahmed Alkhayyat ◽  
...  
Keyword(s):  
Spike Sorting ◽  
Sorting Algorithms ◽  
Neural Spike

scholarly journals SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeremy Magland ◽  
James J Jun ◽  
Elizabeth Lovero ◽  
Alexander J Morley ◽  
Cole Lincoln Hurwitz ◽  
...  
Keyword(s):  
Ground Truth ◽  
Brain Regions ◽  
Optimal Choice ◽  
Spike Sorting ◽  
Experimental Conditions ◽  
Total Size ◽  
Sorting Algorithms ◽  
Electrophysiological Recordings ◽  
Wide Range ◽  
Electrophysiological Studies

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

scholarly journals SHYBRID: A graphical tool for generating hybrid ground-truth spiking data for evaluating spike sorting performance

2019 ◽  
Author(s):  
Jasper Wouters ◽  
Fabian Kloosterman ◽  
Alexander Bertrand
Keyword(s):  
Ground Truth ◽  
Neural Recording ◽  
Data Driven ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Device ◽  
Ground Truth Data ◽  
Sorting Algorithms ◽  
Graphical Tool ◽  
Fine Tune

AbstractSpike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

scholarly journals Efficient neural spike sorting using data subdivision and unification

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0245589
Author(s):  
Masood Ul Hassan ◽  
Rakesh Veerabhadrappa ◽  
Asim Bhatti
Keyword(s):  
Clustering Algorithms ◽  
Spike Sorting ◽  
Technological Advancement ◽  
Feature Sets ◽  
Electrophysiological Data ◽  
Sorting Algorithms ◽  
Neural Spike ◽  
The Brain

Neural spike sorting is prerequisite to deciphering useful information from electrophysiological data recorded from the brain, in vitro and/or in vivo. Significant advancements in nanotechnology and nanofabrication has enabled neuroscientists and engineers to capture the electrophysiological activities of the brain at very high resolution, data rate and fidelity. However, the evolution in spike sorting algorithms to deal with the aforementioned technological advancement and capability to quantify higher density data sets is somewhat limited. Both supervised and unsupervised clustering algorithms do perform well when the data to quantify is small, however, their efficiency degrades with the increase in the data size in terms of processing time and quality of spike clusters being formed. This makes neural spike sorting an inefficient process to deal with large and dense electrophysiological data recorded from brain. The presented work aims to address this challenge by providing a novel data pre-processing framework, which can enhance the efficiency of the conventional spike sorting algorithms significantly. The proposed framework is validated by applying on ten widely used algorithms and six large feature sets. Feature sets are calculated by employing PCA and Haar wavelet features on three widely adopted large electrophysiological datasets for consistency during the clustering process. A MATLAB software of the proposed mechanism is also developed and provided to assist the researchers, active in this domain.

Multi-Pattern Recognition Through Maximization of Signal-to-Peak-Interference Ratio With Application to Neural Spike Sorting

2020 ◽  
Vol 68 ◽  
pp. 6240-6254
Author(s):  
Jasper Wouters ◽  
Panagiotis Patrinos ◽  
Fabian Kloosterman ◽  
Alexander Bertrand
Keyword(s):  
Pattern Recognition ◽  
Spike Sorting ◽  
Neural Spike

Safety Assessment of Long Term Serviced Pressure Vessels: A Case Study of Typical Refining and Chemical Plants in China

2021 ◽  
Author(s):  
Zhiyuan Han ◽  
Guoshan Xie ◽  
Haiyi Jiang ◽  
Xiaowei Li
Keyword(s):  
Pressure Vessel ◽  
Safety Assessment ◽  
Failure Modes ◽  
Pressure Vessels ◽  
Time Dependent ◽  
Chemical Plants ◽  
Safety Specification ◽  
Detailed Assessment

Abstract The safety and risk of the long term serviced pressure vessels, especially which serviced more than 20 years, has become one of the most concerned issues in refining and chemical industry and government safety supervision in China. According to the Chinese pressure vessel safety specification TSG 21-2016 “Supervision Regulation on Safety Technology for Stationary Pressure Vessel”, if necessary, safety assessment should be performed for the pressure vessel which reaches the design service life or exceeds 20 years without a definite design life. However, the safety and risk conditions of most pressure vessels have little changes after long term serviced because their failure modes are time-independent. Thus the key problem is to identify the devices with the time-dependent failure modes and assess them based on the failure modes. This study provided a case study on 16 typical refining and chemical plants including 1870 pressure vessels serviced more than 20 years. The quantitative risk and damage mechanisms were calculated based on API 581, the time-dependent and time-independent failure modes were identified, and the typical pressure vessels were assessed based on API 579. Taking the high pressure hydrogenation plant as an example, this study gave the detailed assessment results and conclusions. The results and suggestions in this study are essential for the safety supervision and extending life of long term serviced pressure vessels in China.

scholarly journals Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces

2019 ◽  
Author(s):  
Benyamin Haghi ◽  
Spencer Kellis ◽  
Sahil Shah ◽  
Maitreyi Ashok ◽  
Luke Bashford ◽  
...  
Keyword(s):  
Neural Network ◽  
Posterior Parietal Cortex ◽  
Ground Truth ◽  
Open Loop ◽  
Neural Data ◽  
State Dynamic ◽  
History Of ◽  
Posterior Parietal ◽  
Machine Interface ◽  
Memory Accesses

AbstractWe present a new deep multi-state Dynamic Recurrent Neural Network (DRNN) architecture for Brain Machine Interface (BMI) applications. Our DRNN is used to predict Cartesian representation of a computer cursor movement kinematics from open-loop neural data recorded from the posterior parietal cortex (PPC) of a human subject in a BMI system. We design the algorithm to achieve a reasonable trade-off between performance and robustness, and we constrain memory usage in favor of future hardware implementation. We feed the predictions of the network back to the input to improve prediction performance and robustness. We apply a scheduled sampling approach to the model in order to solve a statistical distribution mismatch between the ground truth and predictions. Additionally, we configure a small DRNN to operate with a short history of input, reducing the required buffering of input data and number of memory accesses. This configuration lowers the expected power consumption in a neural network accelerator. Operating on wavelet-based neural features, we show that the average performance of DRNN surpasses other state-of-the-art methods in the literature on both single- and multi-day data recorded over 43 days. Results show that multi-state DRNN has the potential to model the nonlinear relationships between the neural data and kinematics for robust BMIs.

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