Past, present and future of spike sorting techniques

Rey, Hernán G.; Pedreira, Carlos Eduardo; Quiroga, Rodrigo Quian

doi:10.1016/j.brainresbull.2015.04.007

Cited by 388 publications

(380 citation statements)

References 82 publications

Supporting

Mentioning

374

Contrasting

Order By: Relevance

“…2B). Recording the spikes of the same neuron on multiple, closely located electrodes significantly enhances the quality of spike sorting and increases the single-unit yield (Gray et al 1995;Harris et al 2000;Quiroga 2012;Rey et al 2015). On the other hand, with a different configuration, where the recording sites are selected apart from each other with equal spacing (Fig.…”

Section: Electrophysiological Recording Capabilities Of the Edc Systemmentioning

confidence: 99%

Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe

Fiáth

Beregszászi

Horváth

et al. 2016

Journal of Neurophysiology

View full text Add to dashboard Cite

Fiáth R, Beregszászi P, Horváth D, Wittner L, Aarts AA, Ruther P, Neves HP, Bokor H, Acsády L, Ulbert I. Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe. J Neurophysiol 116: 2312-2330. First published August 17, 2016 doi:10.1152/jn.00318.2016.-Recording simultaneous activity of a large number of neurons in distributed neuronal networks is crucial to understand higher order brain functions. We demonstrate the in vivo performance of a recently developed electrophysiological recording system comprising a two-dimensional, multi-shank, high-density silicon probe with integrated complementary metal-oxide semiconductor electronics. The system implements the concept of electronic depth control (EDC), which enables the electronic selection of a limited number of recording sites on each of the probe shafts. This innovative feature of the system permits simultaneous recording of local field potentials (LFP) and single-and multiple-unit activity (SUA and MUA, respectively) from multiple brain sites with high quality and without the actual physical movement of the probe. To evaluate the in vivo recording capabilities of the EDC probe, we recorded LFP, MUA, and SUA in acute experiments from cortical and thalamic brain areas of anesthetized rats and mice. The advantages of large-scale recording with the EDC probe are illustrated by investigating the spatiotemporal dynamics of pharmacologically induced thalamocortical slow-wave activity in rats and by the two-dimensional tonotopic mapping of the auditory thalamus. In mice, spatial distribution of thalamic responses to optogenetic stimulation of the neocortex was examined. Utilizing the benefits of the EDC system may result in a higher yield of useful data from a single experiment compared with traditional passive multielectrode arrays, and thus in the reduction of animals needed for a research study. electronic depth control; silicon probe; high-density recording; thalamocortical oscillation; optogenetics DESPITE THE RAPID ADVANCEMENT of brain imaging techniques offering both high spatial and temporal resolution, electrophysiological tools are still widely used methods to investigate the complex spatiotemporal activity patterns of neuronal circuits. Over the past few decades, single-wire electrodes used for in vivo extracellular recording of action potentials evolved into multielectrode arrays covering the range of up to 1,000 recording sites NEW & NOTEWORTHY Recording the electrical activity of a large number of neurons located in the thalamocortical

show abstract

Section: Electrophysiological Recording Capabilities Of the Edc Systemmentioning

confidence: 99%

Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe

Fiáth

Beregszászi

Horváth

et al. 2016

Journal of Neurophysiology

View full text Add to dashboard Cite

show abstract

“…Neural signal decoding is a critical signal processing stage required by CNS-and PNS-based neuroprostheses, mainly based on spike sorting algorithms to analyse the single neuron activity from extra-cellular recordings [5]. Thanks to the evolution of the neuronal interface at the PNS level with intrafascicular electrodes characterized by a good selectivity [6], in principle it is possible to perform decoding on the PNS with techniques similar to those exploited for the CNS.…”

Section: Motivation and Backgroundmentioning

confidence: 99%

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

Pani

Barabino

Citi

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Abstract-The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP).The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 hours processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

show abstract

“…Multi-unit activity represents the pooled spiking activity of many neurons recorded on a single electrode. It can be generated simply by thresholding extracellular voltage signals, and thus avoids computationally costly spike sorting algorithms [40]. It has been shown that in the motor cortex multi-unit activity of a single channel may carry a similar amount of information about a movement as the single units extracted from the same recording channel [3], [41].…”

Section: Discussionmentioning

confidence: 99%

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

Lungu¹,

Riehle²,

Nawrot³

et al. 2017

IBM J. Res. & Dev.

View full text Add to dashboard Cite

Neurons in mammalian motor cortex encode physical parameters of voluntary movements during planning and execution of a motor task. Brain-machine interfaces can decode limb movements from the activity of these neurons in real time. The future goal is to control prosthetic devices in severely paralyzed patients or to restore communication if the ability to speak or make gestures is lost. Here, we implemented a spiking neural network that decodes movement intentions from the activity of individual neurons recorded in the motor cortex of a monkey. The network runs on neuromorphic hardware and performs its computations in a purely spike-based fashion. It incorporates an insect-brain-inspired, three-layer architecture with 176 neurons. Cortical signals are filtered using lateral inhibition, and the network is trained in a supervised fashion to predict two opposing directions of the monkey's arm reaching movement before the movement is carried out. Our network operates on the actual spikes that have been emitted by motor cortical neurons, without the need to construct intermediate non-spiking representations. Using a pseudo-population of 12 manually-selected neurons, it reliably predicts the movement direction with an accuracy of 89.32 % on unseen data after only 100 training trials. Our results provide a proof of concept for the first-time use of a neuromorphic device for decoding movement intentions.

show abstract

Past, present and future of spike sorting techniques

Cited by 388 publications

References 82 publications

Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe

Large-scale recording of thalamocortical circuits: in vivo electrophysiology with the two-dimensional electronic depth control silicon probe

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

Predicting voluntary movements from motor cortical activity with neuromorphic hardware

Contact Info

Product

Resources

About