scholarly journals Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield

2020 ◽  
Author(s):  
John Choi ◽  
Katie Wingel ◽  
Adam Charles ◽  
Krishan Kumar ◽  
Mahdi Choudhury ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Adaptive Methods ◽  
Spike Timing ◽  
Limiting Factor ◽  
Electrode Arrays ◽  
Neural Signals ◽  
Selection Strategies ◽  
Recording Channels ◽  
Recording Channel ◽  
Electrode Selection

AbstractNeural-Matrix style, high-density electrode arrays for brain-machine interfaces (BMIs) and neuroscientific research require the use of multiplexing: Each recording channel can be routed to one of several electrode sites on the array. This capability allows the user to flexibly distribute recording channels to the locations where the most desirable neural signals can be resolved. For example, in the Neuropixel probe, 960 electrodes can be addressed by 384 recording channels. However, currently no adaptive methods exist to use recorded neural data to optimize/customize the electrode selections per recording context. Here, we present an algorithm called classification-based selection (CBS) that optimizes the joint electrode selections for all recording channels so as to maximize isolation quality of detected neurons. We show, in experiments using Neuropixels in non-human primates, that this algorithm yields a similar number of isolated neurons as would be obtained if all electrodes were recorded simultaneously. Neuron counts were 41-85% improved over previously published electrode selection strategies. The neurons isolated from electrodes selected by CBS were a 73% match, by spike timing, to the complete set of recordable neurons around the probe. The electrodes selected by CBS exhibited higher average per-recording-channel signal-to-noise ratio. CBS, and selection optimization in general, could play an important role in development of neurotechnologies for BMI, as signal bandwidth becomes an increasingly limiting factor. Code and experimental data have been made available1.

ENHANCING THE YIELD OF HIGH-DENSITY ELECTRODE ARRAYS THROUGH AUTOMATED ELECTRODE SELECTION

2012 ◽  
Vol 22 (01) ◽  
pp. 1-19 ◽  
Author(s):  
GERT VAN DIJCK ◽  
KARSTEN SEIDL ◽  
OLIVER PAUL ◽  
PATRICK RUTHER ◽  
MARC M. VAN HULLE ◽  
...  
Keyword(s):  
Control Algorithm ◽  
Signal To Noise Ratio ◽  
Pyramidal Cells ◽  
Inhibitory Interneurons ◽  
Electrode Arrays ◽  
Signal To Noise ◽  
Detection Algorithms ◽  
Benchmark Data ◽  
Depth Control ◽  
Electrode Selection

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.

scholarly journals Multi-shanks SiNAPS Active Pixel Sensor CMOS probe: 1024 simultaneously recording channels for high-density intracortical brain mapping

2019 ◽  
Author(s):  
Fabio Boi ◽  
Nikolas Perentos ◽  
Aziliz Lecomte ◽  
Gerrit Schwesig ◽  
Stefano Zordan ◽  
...  
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Brain Area ◽  
Large Population ◽  
Brain Structures ◽  
High Density ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Unit Recording ◽  
Recording Channels

AbstractThe advent of implantable active dense CMOS neural probes opened a new era for electrophysiology in neuroscience. These single shank electrode arrays, and the emerging tailored analysis tools, provide for the first time to neuroscientists the neurotechnology means to spatiotemporally resolve the activity of hundreds of different single-neurons in multiple vertically aligned brain structures. However, while these unprecedented experimental capabilities to study columnar brain properties are a big leap forward in neuroscience, there is the need to spatially distribute electrodes also horizontally. Closely spacing and consistently placing in well-defined geometrical arrangement multiple isolated single-shank probes is methodologically and economically impractical. Here, we present the first high-density CMOS neural probe with multiple shanks integrating thousand’s of closely spaced and simultaneously recording microelectrodes to map neural activity across 2D lattice. Taking advantage from the high-modularity of our electrode-pixels-based SiNAPS technology, we realized a four shanks active dense probe with 256 electrode-pixels/shank and a pitch of 28 µm, for a total of 1024 simultaneously recording channels. The achieved performances allow for full-band, whole-array read-outs at 25 kHz/channel, show a measured input referred noise in the action potential band (300-7000 Hz) of 6.5 ± 2.1µVRMS, and a power consumption <6 µW/electrode-pixel. Preliminary recordings in awake behaving mice demonstrated the capability of multi-shanks SiNAPS probes to simultaneously record neural activity (both LFPs and spikes) from a brain area >6 mm2, spanning cortical, hippocampal and thalamic regions. High-density 2D array enables combining large population unit recording across distributed networks with precise intra- and interlaminar/nuclear mapping of the oscillatory dynamics. These results pave the way to a new generation of high-density and extremely compact multi-shanks CMOS-probes with tunable layouts for electrophysiological mapping of brain activity at the single-neurons resolution.

scholarly journals Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays

2021 ◽  
Author(s):  
Caleb Scheffer Sponheim ◽  
Vasileios Papadourakis ◽  
Jennifer Collinger ◽  
John Downey ◽  
Jeffrey M Weiss ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Rhesus Macaques ◽  
Human Subjects ◽  
Electrode Arrays ◽  
Computer Interfaces ◽  
Somatosensory Cortices ◽  
Large Numbers ◽  
Electrode Length ◽  
Human Participants

Abstract Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have FDA clearance for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost 9 years of experiments, totaling 17 rhesus macaques (Macaca Mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main Results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization.

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 Improving electrocorticograms of awake and anaesthetized mice using wavelet denoising

2018 ◽  
Vol 4 (1) ◽  
pp. 469-472 ◽  
Author(s):  
Michael Schweigmann ◽  
Klaus Peter Koch ◽  
Fabian Auler ◽  
Frank Kirchhoff
Keyword(s):  
Signal To Noise Ratio ◽  
Wavelet Denoising ◽  
Functional Evaluation ◽  
Electrode Impedance ◽  
Electrode Arrays ◽  
Brain Surface ◽  
Micro Electrode Arrays ◽  
Cellular Circuits ◽  
Noise Models

AbstractThe quality of bioelectrical signals is essential for functional evaluation of cellular circuits. The electrical activity recorded from the cortical brain surface represents the average of many individual synaptic processes. By downsizing micro-electrode arrays, the spatial resolution of electrocortico-grams (ECoGs) can be increased. But, upon increasing electrode impedance, recorded noise from the electrode-tissue interface and the surroundings will become more prominent. Frequently, signal interpretation is improved by post-processing using filtering or pattern recognition. For a variety of applications, wavelet denoising has become an accepted tool. Here, we present how wavelet denoising affects the signal-to-noise ratio of ECoGs. The recording qualities from awake and anesthetized mice was artificially reduced by adding two noise models prior to filtering. Raw and filtered signals were compared by calculating the linear correlation coefficient.

scholarly journals Suppression of Parvalbumin Interneuron Activity in the Prefrontal Cortex Recapitulates Features of Impaired Excitatory/Inhibitory Balance and Sensory Processing in Schizophrenia

2020 ◽  
Vol 46 (4) ◽  
pp. 981-989 ◽  
Author(s):  
Oana Toader ◽  
Moritz von Heimendahl ◽  
Niklas Schuelert ◽  
Wiebke Nissen ◽  
Holger Rosenbrock
Keyword(s):  
Prefrontal Cortex ◽  
Signal To Noise Ratio ◽  
Treatment Options ◽  
Brain Regions ◽  
Inhibitory Interneuron ◽  
Network Activity ◽  
Limiting Factor ◽  
Neuronal Populations ◽  
Parvalbumin Interneuron ◽  
New Treatment

Abstract Accumulating evidence supports parvalbumin expressing inhibitory interneuron (PV IN) dysfunction in the prefrontal cortex as a cause for cognitive impairment associated with schizophrenia (CIAS). PV IN decreased activity is suggested to be the culprit for many of the EEG deficits measured in patients, which correlate with deficits in working memory (WM), cognitive flexibility and attention. In the last few decades, CIAS has been recognized as a heavy burden on the quality of life of patients with schizophrenia, but little progress has been made in finding new treatment options. An important limiting factor in this process is the lack of adequate preclinical models and an incomplete understanding of the circuits engaged in cognition. In this study, we back-translated an auditory stimulation protocol regularly used in human EEG studies into mice and combined it with optogenetics to investigate the role of prefrontal cortex PV INs in excitatory/inhibitory balance and cortical processing. We also assessed spatial WM and reversal learning (RL) during inhibition of prefrontal cortex PV INs. We found significant impairments in trial-to-trial reliability, increased basal network activity and increased oscillation power at 20–60 Hz, and a decreased signal-to-noise ratio, but no significant impairments in behavior. These changes reflect some but not all neurophysiological deficits seen in patients with schizophrenia, suggesting that other neuronal populations and possibly brain regions are involved as well. Our work supports and expands previous findings and highlights the versatility of an approach that combines innovative technologies with back-translated tools used in humans.

scholarly journals Multichannel parallel processing of neural signals in memristor arrays

2020 ◽  
Vol 6 (41) ◽  
pp. eabc4797
Author(s):  
Zhengwu Liu ◽  
Jianshi Tang ◽  
Bin Gao ◽  
Xinyi Li ◽  
Peng Yao ◽  
...  
Keyword(s):  
Signal Processing ◽  
Parallel Processing ◽  
Power Efficiency ◽  
Computational Cost ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Neural Interfaces ◽  
Neural Signals ◽  
Neural Signal ◽  
Recording Channels

Fully implantable neural interfaces with massive recording channels bring the gospel to patients with motor or speech function loss. As the number of recording channels rapidly increases, conventional complementary metal-oxide semiconductor (CMOS) chips for neural signal processing face severe challenges on parallelism scalability, computational cost, and power consumption. In this work, we propose a previously unexplored approach for parallel processing of multichannel neural signals in memristor arrays, taking advantage of their rich dynamic characteristics. The critical information of neural signal waveform is extracted and encoded in the memristor conductance modulation. A signal segmentation scheme is developed to adapt to device variations. To verify the fidelity of the processed results, seizure prediction is further demonstrated, with high accuracy above 95% and also more than 1000× improvement in power efficiency compared with CMOS counterparts. This work suggests that memristor arrays could be a promising multichannel signal processing module for future implantable neural interfaces.

scholarly journals Drug targeting to decrease cardiotoxicity – determination of the cytotoxic effect of GnRH-based conjugates containing doxorubicin, daunorubicin and methotrexate on human cardiomyocytes and endothelial cells

2018 ◽  
Vol 14 ◽  
pp. 1583-1594 ◽  
Author(s):  
Livia Polgár ◽  
Eszter Lajkó ◽  
Pál Soós ◽  
Orsolya Láng ◽  
Marilena Manea ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Anticancer Drug ◽  
Drug Targeting ◽  
Cytotoxic Effect ◽  
Cell Types ◽  
Chemotherapeutic Agents ◽  
Limiting Factor ◽  
Electrode Arrays ◽  
Human Cardiomyocytes

Background: Cardiomyopathy induced by the chemotherapeutic agents doxorubicin and daunorubicin is a major limiting factor for their application in cancer therapy. Chemotactic drug targeting potentially increases the tumor selectivity of drugs and decreases their cardiotoxicity. Increased expression of gonadotropin-releasing hormone (GnRH) receptors on the surface of tumor cells has been reported. Thus, the attachment of the aforementioned chemotherapeutic drugs to GnRH-based peptides may result in compounds with increased therapeutic efficacy. The objective of the present study was to examine the cytotoxic effect of anticancer drug–GnRH-conjugates against two essential cardiovascular cell types, such as cardiomyocytes and endothelial cells. Sixteen different previously developed GnRH-conjugates containing doxorubicin, daunorubicin and methotrexate were investigated in this study. Their cytotoxicity was determined on primary human cardiac myocytes (HCM) and human umbilical vein endothelial cells (HUVEC) using the xCELLigence SP system, which measures impedance changes caused by adhering cells on golden electrode arrays placed at the bottom of the wells. Slopes of impedance–time curves were calculated and for the quantitative determination of cytotoxicity, the difference to the control was analysed. Results: Doxorubicin and daunorubicin exhibited a cytotoxic effect on both cell types, at the highest concentrations tested. Doxorubicin-based conjugates (AN-152, GnRH-III(Dox-O-glut), GnRH-III(Dox-glut-GFLG) and GnRH-III(Dox=Aoa-GFLG) showed the same cytotoxic effect on cardiomyocytes. Among the daunorubicin-based conjugates, [4Lys(Ac)]-GnRH-III(Dau=Aoa), GnRH-III(Dau=Aoa-YRRL), {GnRH-III(Dau=Aoa-YRRL-C)}2 and {[4 N-MeSer]-GnRH-III(Dau-C)}2 had a significant but decreased cytotoxic effect, while the other conjugates – GnRH-III(Dau=Aoa), GnRH-III(Dau=Aoa-K(Dau=Aoa)), [4Lys(Dau=Aoa)]-GnRH-III(Dau=Aoa), GnRH-III(Dau=Aoa-GFLG), {GnRH-III(Dau-C)}2 and [4 N-MeSer]-GnRH-III(Dau=Aoa) – exerted no cytotoxic effect on cardiomyocytes. Mixed conjugates containing methotrexate and daunorubicin – GnRH-III(Mtx-K(Dau=Aoa)) and [4Lys(Mtx)]-GnRH-III(Dau=Aoa) – showed no cytotoxic effect on cardiomyocytes, as well. Conclusion: Based on these results, anticancer drug–GnRH-based conjugates with no cytotoxic effect on cardiomyocytes were identified. In the future, these compounds could provide a more targeted antitumor therapy with no cardiotoxic adverse effects. Moreover, impedimetric cytotoxicity analysis could be a valuable technique to determine the effect of drugs on cardiomyocytes.

SOME ASPECTS OF MULTIPLE RECORDING IN SEISMIC PROSPECTING

Geophysics ◽  
1936 ◽  
Vol 1 (3) ◽  
pp. 365-377 ◽  
Author(s):  
Paul W. Klipsch
Keyword(s):  
Signal To Noise Ratio ◽  
Statistical Distribution ◽  
Frequency Of Occurrence ◽  
Signal To Noise ◽  
Probability Curve ◽  
Seismic Prospecting ◽  
Recording Channel ◽  
Noise Ratio

Considerable attention has been given to the use of more than one geophone on each recording channel with the hope of increasing the signal‐to‐noise ratio, where “signal” is taken to mean “recorded reflection” and “noise” means any undesired recorded amplitude. The average expectancy of this gain is evaluated analytically, and the statistical distribution examined. Since at different instants this gain possesses different values dependent on a large number of causes, these causes are assumed to be random in nature; a justification of this and other assumptions is explained, and the distribution of gain in terms of frequency of occurrence is expressed in the form of a probability curve. From this analysis an answer can be inferred to the question, “if n is the number of geophones, what is the probability that a gain of [Formula: see text] will occur?”.

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