Improving neural spike sorting performance using template enhancement

Zack, Frehlick; Williams, Ian; Constandinou, Timothy G.

doi:10.1109/biocas.2016.7833847

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Moreover, real-time spike sorting allows for experimental conditions that adapt according to the neural responses that are observed which makes room for wider scientific investigation including but not limited to the neural basis of adaptive sensorimotor computation [78,79], contextual information processing [80][81][82][83] and storage [84][85][86][87], navigation [88][89][90], and information transfer in neural circuits [46,48]. Brain-machine-interfaces (BMI), like limb prosthetics, also necessitate that spike sorting is performed in real-time on a time-scale of hundreds of milliseconds [65], as these are usually controlled by direct neuronal signalling that is measured invasively by an array of electrodes [91]. Although, for BMI applications, the exact identity of each spike may actually not be crucial and the spike sorting stage can be omitted entirely [52].…”

Section: Discussionmentioning

confidence: 99%

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

Brouns

Celikel

2019

Preprint

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Thanks to the advancements in multichannel intracranial neural recordings, magnetic neuroimaging and magnetic neurostimulation techniques (including magnetogenetics), it is now possible to perform large-scale high-throughput neural recordings while imaging or controlling neural activity in a magnetic field. Analysis of neural recordings performed in a switching magnetic field, however, is not a trivial task as gradient and pulse artefacts interfere with the unit isolation. Here we introduce a toolbox called PASER, Processing and Analysis Schemes for Extracellular Recordings, that performs automated denoising, artefact removal, quality control of electrical recordings, unit classification and visualization. PASER is written in MATLAB and modular by design. The current version integrates with third party applications to provide additional functionality, including data import, spike sorting and the analysis of local field potentials. After the description of the toolbox, we evaluate 9 different spike sorting algorithms based on computational cost, unit yield, unit quality and clustering reliability across varying conditions including self-blurring and noise-reversal. Implementation of the best performing spike sorting algorithm (KiloSort) in the default version of the PASER provides the end user with a fully automated pipeline for quantitative analysis of broadband extracellular signals.PASER can be integrated with any established pipeline that sample neural activity with intracranial electrodes. Unlike the existing algorithmic solutions, PASER provides an end-to-end solution for neural recordings made in switching magnetic fields independent from the number of electrodes and the duration of recordings, thus enables high-throughput analysis of neural activity in a wide range of electro-magnetic recording conditions.

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Section: Discussionmentioning

confidence: 99%

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

Brouns

Celikel

2019

Preprint

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“…In the near term we are looking at a variety of algorithm enhancements that could improve template matching performance (e.g. by pre-manipulation of the templates [39]) or identify and potentially track changes in templates due to electrode drift and fibrous encapsulation-reducing calibration requirements and enhancing system performance for chronic recordings. While in the long term we would look to leverage commercial technology scaling to enable integration of online naive spike sorting for hundreds of channels-scaling, enhancing and making viable existing approaches [40,41].…”

Section: Future Workmentioning

confidence: 99%

Compact standalone platform for neural recording with real-time spike sorting and data logging

et al. 2018

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The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals-revealing insights that are not attainable through scheduled recording sessions. This system achieves the lowest power per channel to date and provides a robust, low-latency, low-bandwidth and verifiable output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

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“…In the near term we are looking at a variety of algorithm enhancements that could improve template matching performance (e.g. by pre-manipulation of the templates [28]) or identify and potentially track changes in templates due to electrode drift and fibrous encapsulation -reducing calibration requirements and enhancing system performance for chronic recordings. While in the long term we would look to leverage commercial technology scaling to enable integration of on-line naive spike sorting for hundreds of channels -scaling, enhancing and making viable existing approaches [29,30].…”

Section: Future Workmentioning

confidence: 99%

Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging

Luan

Williams

Maslik

et al. 2017

Preprint

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Abstract.Objective. Longitudinal observation of single unit neural activity from large numbers of cortical neurons in awake and mobile animals is often a vital step in studying neural network behaviour and towards the prospect of building effective Brain Machine Interfaces (BMIs). These recordings generate enormous amounts of data for transmission & storage, and typically require offline processing to tease out the behaviour of individual neurons. Our aim was to create a compact system capable of: 1) reducing the data bandwidth by circa 3 orders of magnitude (greatly improving battery lifetime and enabling low power wireless transmission); 2) producing real-time, low-latency, spike sorted data; and 3) long term untethered operation. Approach. We have developed a headstage that operates in two phases. In the short training phase a computer is attached and classic spike sorting is performed to generate templates. In the second phase the system is untethered and performs template matching to create an event driven spike output that is logged to a micro-SD card. To enable validation the system is capable of logging the high bandwidth raw neural signal data as well as the spike sorted data. Main results. The system can successfully record 32 channels of raw neural signal data and/or spike sorted events for well over 24 hours at a time and is robust to power dropouts during battery changes as well as SD card replacement. A 24-hour initial recording in a non-human primate M1 showed consistent spike shapes with the expected changes in neural activity during awake behaviour and sleep cycles. Significance The presented platform allows neural activity to be unobtrusively monitored and processed in real-time in freely behaving untethered animals revealing insights that are not attainable through scheduled recording sessions and provides a robust, low-latency, low-bandwidth output suitable for BMIs, closed loop neuromodulation, wireless transmission and long term data logging.

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Improving neural spike sorting performance using template enhancement

Cited by 6 publications

References 8 publications

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

PASER for automated analysis of neural signals recorded in pulsating magnetic fields

Compact standalone platform for neural recording with real-time spike sorting and data logging

Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging

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