Voltage Traces Research Articles

Neuroelectrophysiology-compatible electrolytic lesioning.

Lesion studies have historically been instrumental for establishing causal connections between brain and behavior. They stand to provide additional insight if integrated with multielectrode techniques common in systems neuroscience. Here, we present and test a platform for creating electrolytic lesions through chronically implanted, intracortical multielectrode probes without compromising the ability to acquire neuroelectrophysiology. A custom-built current source provides stable current and allows for controlled, repeatable lesions in awake-behaving animals. Performance of this novel lesioning technique was validated using histology from ex vivo and in vivo testing, current and voltage traces from the device, and measurements of spiking activity before and after lesioning. This electrolytic lesioning method avoids disruptive procedures, provides millimeter precision over the extent and submillimeter precision over the location of the injury, and permits electrophysiological recording of single-unit activity from the remaining neuronal population after lesioning. This technique can be used in many areas of cortex, in several species, and theoretically with any multielectrode probe. The low-cost, external lesioning device can also easily be adopted into an existing electrophysiology recording setup. This technique is expected to enable future causal investigations of the recorded neuronal population's role in neuronal circuit function, while simultaneously providing new insight into local reorganization after neuron loss.

A cross species thermoelectric and spatiotemporal analysis of alternans in live explanted hearts using dual voltage-calcium fluorescence optical mapping

Objective. Temperature plays a crucial role in influencing the spatiotemporal dynamics of the heart. Electrical instabilities due to specific thermal conditions typically lead to early period-doubling bifurcations and beat-to-beat alternans. These pro-arrhythmic phenomena manifest in voltage and calcium traces, resulting in compromised contractile behaviors. In such intricate scenario, dual optical mapping technique was used to uncover unexplored multi-scale and nonlinear couplings, essential for early detection and understanding of cardiac arrhythmia. Approach. We propose a methodological analysis of synchronized voltage-calcium signals for detecting alternans, restitution curves, and spatiotemporal alternans patterns under different thermal conditions, based on integral features calculation. To validate our approach, we conducted a cross-species investigation involving rabbit and guinea pig epicardial ventricular surfaces and human endocardial tissue under pacing-down protocols. Main results. We show that the proposed integral feature, as the area under the curve, could be an easily applicable indicator that may enhance the predictability of the onset and progression of cardiac alternans. Insights into spatiotemporal correlation analysis of characteristic spatial lengths across different heart species were further provided. Significance. Exploring cross-species thermoelectric features contributes to understanding temperature-dependent proarrhythmic regimes and their implications on coupled spatiotemporal voltage-calcium dynamics. The findings provide preliminary insights and potential strategies for enhancing arrhythmia detection and treatment.

Single-unit data for sensory neuroscience: Responses from the auditory nerve of young-adult and aging gerbils

This dataset was collected to study the functional consequences of age-related hearing loss for the auditory nerve, which carries acoustic information from the periphery to the central auditory system. Using high-impedance glass electrodes, raw voltage traces and spike times were recorded from more than one thousand single fibres of the auditory nerve of young-adult, middle-aged, and old Mongolian gerbils raised in a quiet environment. The dataset contains not only responses to simple acoustic stimuli to characterize the fibres, but also to more complex stimuli, such as speech logatomes in background noise and Schroeder-phase stimuli. A software toolbox is provided to search through the dataset, to plot various analysed outcomes, and to give insight into the analyses. This dataset may serve as a valuable resource to test further hypotheses about age-related hearing loss. Additionally, it can aid in optimizing available computational models of the auditory system, which can contribute to, or eventually even fully replace, animal experiments.

Performance degradation due to thermal cycling in soldered Faraday and SuperPower HTS tape stacks

Soldering stacks of high temperature superconducting (HTS) tapes results in a current carrying matrix with high electrical, thermal, and mechanical stability. In some applications, these tape stacks may be exposed to elevated temperatures during thermal heat cycles. Previous research on thermal degradation of high temperature superconductors has been performed with isolated crystals or individual HTS tapes, showing that oxygen-out diffusion from the rare-earth barium copper oxide (REBCO) results in a reduction of the superconducting performance. In this work, the performance of Pb37Sn63 soldered HTS tape stacks with thermal cycling to 170∘ C is quantified through determination of the critical current Ic , the n-value, the linear resistance R in the pre-transition region of the tape stack current–voltage (I–V) traces, and the diffusion coefficient D associated with oxygen-out diffusion. The effect of tape manufacturer and nickel electroplating were considered; three soldered stacks each of unplated Faraday, nickel electroplated Faraday, unplated SuperPower, and nickel electroplated SuperPower tapes were fabricated. Across 20–30 one hour thermal cycles, there are no significant differences in degradation profiles observed between the unplated and nickel electroplated tape stacks for a given manufacturer. There is however a marked difference in degradation profiles observed between the SuperPower and Faraday tape stacks. For the unplated and plated SuperPower tape stacks, the degradation in Ic generally follows the model to describe oxygen-out diffusion from the superconducting REBCO layer. Significantly less degradation is observed in the unplated and plated Faraday tape stacks, and in some sections Ic even increases slightly across cycles. Further, cases of increasing Ic accompanied with decreasing n are observed, a decoupling between the two parameters. Across all samples, the linear resistance was highest in current-redistribution regions near the leads. Consistent with the model of oxygen-out diffusion and the formation of an increasingly wide oxygen deficient layer in the REBCO, the linear resistance increased with cycle number.

Empirical modeling and prediction of neuronal dynamics

Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although less realistic, have also contributed to understand neuronal dynamics. However, there is still a vast volume of data that have not been associated with a mathematical model, mainly because data are acquired more rapidly than they can be analyzed or because it is difficult to analyze (for instance, if the number of ionic channels involved is huge). Therefore, developing new methodologies to obtain mathematical or computational models associated with data (even without previous knowledge of the source) can be helpful to make future predictions. Here, we explore the capability of a wavelet neural network to identify neuronal (single-cell) dynamics. We present an optimized computational scheme that trains the ANN with biologically plausible input currents. We obtain successful identification for data generated from four different neuron models when using all variables as inputs of the network. We also show that the empiric model obtained is able to generalize and predict the neuronal dynamics generated by variable input currents different from those used to train the artificial network. In the more realistic situation of using only the voltage and the injected current as input data to train the network, we lose predictive ability but, for low-dimensional models, the results are still satisfactory. We understand our contribution as a first step toward obtaining empiric models from experimental voltage traces.

On Credibility of Adversarial Examples Against Learning-Based Grid Voltage Stability Assessment

Voltage stability assessment is essential for maintaining reliable power grid operations. Stability assessment approaches using deep learning address the shortfalls of the traditional time-domain simulation-based approaches caused by increased system complexity. However, deep learning models are shown to be vulnerable to adversarial examples in the field of computer vision. While this vulnerability has been noticed by the power grid cybersecurity research, the domain-specific analysis on the requirements imposed upon effective attack implementation is still lacking. Although these attack requirements are usually reasonable in computer vision tasks, they can be stringent in the context of power grids. In this paper, we conduct a systematic investigation on the attack requirements and credibility of six representative adversarial example attacks based on a voltage stability assessment application for the New England 10-machine 39-bus power system. We show that (1) compromising about half the transmission system buses' voltage traces is a rule-of-thumb attack requirement; (2) the universal adversarial perturbations regardless of the original clean voltage trajectory possess the same credibility as the widely studied false data injection attacks on power grid state estimation, while the input-specific adversarial perturbations are less credible; (3) the prevailing strong adversarial training thwarts the universal perturbations but fails in defending certain input-specific perturbations. To advance defense to cope with both universal and input-specific adversarial examples, we propose a new approach that simultaneously estimates the predictive uncertainty of any given input of voltage trajectory and thwarts the attacks effectively.

Erythropoietin in Neonates with Perinatal Asphyxia Undergoing Therapeutic Hypothermia—A Prospective Cohort Study

Aims To assess the safety and feasibility of erythropoietin (EPO) in asphyxiated neonates undergoing therapeutic hypothermia (TH). Subjects and methods This study enrolled 60 neonates with HIE undergoing TH. 30 neonates were divided into EPO with TH group, where neonates received EPO (dose), at a dose rate of 1000 IU/kg/48 hours, for four doses and the remaining 30 neonates undergoing TH were into control group. Magnetic resonance imaging brain was undertaken between 10 and 14 days of life in surviving neonates. The Bayley Scales of Infant (BSI) Development IV was performed at regular intervals up to the age of 12 months. Results Amplitude-integrated electroencephalogram (aEEG) showed burst suppression (19% vs. 11%), low voltage (10% vs. 4%), and flat trace (7% vs. 3%) in control group in comparison with EPO concurrent with TH group which was statistically significant. Brain magnetic-resonance imaging (MRI) done at 12 days (±2 days) showed significant brain injury patterns such as severe brain injury (4% vs. 9% P = .05) and regional specific HIE (7% vs. 13%, P = .03) in control group of only TH neonates. At 12 months, neurodevelopment outcomes in EPO with TH group neonates showed a favorable outcome. Conclusion rhEPO concurrent with TH in HIE neonates resulted in significantly less severe brain injuries in MRI brain in HIE neonates. aEEG changes were less statistically significant in EPO with TH group with favorable neurodevelopmental outcomes at 12 months of age.

Insights into soft short circuit-based degradation of lithium metal batteries.

The demand for electric vehicles with extended ranges has created a renaissance of interest in replacing the common metal-ion with higher energy-density metal-anode batteries. However, the potential battery safety issues associated with lithium metal must be addressed to enable lithium metal battery chemistries. A considerable performance gap between lithium (Li) symmetric cells and practical Li batteries motivated us to explore the correlation between the shape of voltage traces and degradation. We coupled impedance spectroscopy and operando NMR and used the new approach to show that transient (i.e., soft) shorts form in realistic conditions for battery applications; however, they are typically overlooked, as their electrochemical signatures are often not distinct. The typical rectangular-shaped voltage trace, widely considered ideal, was proven, under the conditions studied here, to be a result of soft shorts. Recoverable soft-shorted cells were demonstrated during a symmetric cell polarisation experiment, defining a new type of critical current density: the current density at which the soft shorts are not reversible. Moreover, we demonstrated that soft shorts, detected via electrochemical impedance spectroscopy (EIS) and validated via operando NMR, are predictive towards the formation of hard shorts, showing the potential use of EIS as a relatively low-cost and non-destructive method for early detection of catastrophic shorts and battery failure while demonstrating the strength of operando NMR as a research tool for metal plating in lithium batteries.

APPLICATION OF AMPLITUDE-INTEGRATED ELECTROENCEPHALOGRAPHY IN PATIENTS OF NEONATAL INTENSIVE CARE UNITS

Intriduction. Amplitude-integrated electroencephalography is a technique for monitoring continuous cerebral function, when electroencephalographic signal is filtered, scaled and compressed in time.
 Objective of the study is to elucidate peculiarities of interpreting findings of the amplitude-integrated electroencephalography in patients of neonatal intensive care units, and represent a clinical case of establishing diagnosis by using the amplitude-integrated electroencephalography for a critically sick neonate.
 Materials and methods. A comprehensive literature review was conducted across international and national databases, including Elsevier, PubMed, Medline, Web of Science, Scopus, Cochrane Central Register of Controlled Trials, and Google Scholar, covering the period from 2019 to 2023. The search focused on the keywords "amplitude-integrated electroencephalography" and/or "aEEG" and "neonates" and/or "term infants" and/or "preterm infants." The clinical case presented took place at the neonatal intensive care unit of the Maternity Home "Central Municipal Clinical Hospital", Chernivtsi, Ukraine. Permission for publication was obtained from both parents of the child.
 Results. The main indications for conducting the amplitude-integrated electroencephalography in neonates include the assessment of the cerebral function and the degree of cerebral damage with hypoxic-ischemic encephalopathy or asphyxia at birth (often combined with therapeutic hypothermia); assessment of sleep-wake cycle; identification of seizures; assessment of cerebral function maturity in preterm neonates.
 The underlying cerebral activity is estimated according to the amplitude-integrated electroencephalography findings by means of a simple recognition of visual images corresponding to the five main patterns: Continuous Normal Voltage, Discontinuous Normal Voltage, Burst Suppression, Low Voltage, and Flat Trace. A typical neonatal single seizure fit on the amplitude-integrated electroencephalography looks like a “hump” or lower edge elevation interrupting the background recording. Repeated fits (epileptic status) look like a “saw” of repeated “humps”, one of each represents one attack. The article presents a clinical case of the diagnostic search in the neonate with congenital pneumonia and development of multiple organ failure syndrome including seizure syndrome. Clinical tonic-clonic seizures of the infant were associated with electroencephalographic criteria of status epilepticus both on the amplitude-integrated electroencephalography (repeated symmetrical elevations of the lower edge in the left and right) and on the standard electroencephalography (different variations of seizure graphic elements in the right and left). Considering the results of the clinical and instrumental examination, an adequate anticonvulsant therapy was administered.
 Conclusions. Continuous recording of the video-amplitude-integrated electroencephalography and standard electroencephalography in infants from the risk group is a good strategy of the neurological status effective monitoring. It enables to assess the underlying cerebral electric activity and it maturity, diagnose seizures and manage anticonvulsant therapy correctly. An adequate training of the staff who are at the patient’s bedside 24/7 is an important part of the interdisciplinary collaboration which is essential for a safe and effective management of patients in the neonatal intensive care units, prevention of early complications and disability in the future.

Analysis of the effects of complex electrode geometries on the energy deposition and temporally and spatially averaged electric field measurements of surface dielectric barrier discharges

AbstractSurface dielectric barrier discharges (SDBDs) have been gaining interest in part due to their scalability and flexibility of materials used, allowing larger electrodes with more complex geometries. This paper seeks to elucidate the properties of SDBD geometries utilizing differing repeated lattice structures. Voltage and current traces, optical emission spectroscopy, digital imaging, and numerical analysis are used to analyze the electrodes. Temporally and spatially averaged reduced electric fields and the total power deposited into the plasma are presented. The averaged reduced electric field is not significantly affected by increasing applied voltage, but minor variations could be observed due to the geometry of the electrode lattice structures. Finally, plasma power does not track linearly with perimeter in these more complicated lattice structures.

Thermal sensing capability and current–voltage–temperature characteristics in Pt/n-GaP/Al/Ti Schottky diodes

We have discussed the thermal sensing capability under a constant current level and current versus voltage (I–V) traces by measuring the temperature of high series resistance Pt/n-GaP/Al/Ti Schottky structures in the 100−320 K range. The Rs values of 35 Ω and 4.50 × 103 kΩ for the device have been determined from I–V traces at 320 and 100 K, respectively. The thermal sensing (V–T) curves are expected to give a straight line at each current level. However, the V–T curves have deviated upward from linearity due to the high Rs value of the device after a certain temperature. The deviation point from linearity in V–T traces shifts to higher temperatures with an increase in bias voltage and current level. Thereby, the straight-line interval portion of the V–T curve has become too small with an increase in the current value. The thermal sensing coefficient α changed from 2.49 mV/K at 10 μA to 3.21 mV/K at 0.50 nA. Therefore, it has been concluded that the Pt/n-GaP/Al/Ti Schottky barrier (SB) is preferable for thermal sensor applications at the small current levels of 0.50, 1.0, 2.0, and 10.0 nA with high sensitivity up to a minimum temperature of 100 K. From I–V curves, qΦb0 and ideality factor values have ranged from 1.200 eV and 1.066 at 320 K to 0.854 eV and 1.705 at 100 K. It has been reported in the literature that the large SB height leads to a better temperature response.

Detection of DNA translocations in a nanopore series circuit using a current clamp.

The advancement of nanopore sensing technology over the past 20 years has been impressive, particularly in the field of nucleic acid sequencing, which has already been used in commercial diagnostic tests. A traditional configuration of nanopore sensing records the current through a single nanopore using a voltage clamp, which hits a bottleneck in expanding its functions, while integrating several nanopores to build a nanopore circuit may be an effective solution. Here, we report a new strategy combining a nanopore series circuit and a current clamp to record the current signal and the voltage signal of DNA translocation through a nanopore simultaneously, which could increase the fidelity of event analysis. We observed a capacitor-like charging and discharging behavior in the voltage signals and proposed a detailed microscopic mechanism to elucidate it. Our strategy could benefit the development of nanopore technology and contribute to understanding the working principles of the units in a nanopore circuit system.

A Partial Discharge Inception Voltage Modeling Approach

A partial discharge inception voltage modeling approach promoting design-for-reliability considerations in advanced power modules is presented. As power modules are being operated at higher voltages and become more compact to meet power density demands, there is an increased risk of partial discharge, the silent precursor to electrical breakdown that degrades insulation material. The trade-off between voltage class and module compaction must be quantified. This work presents a methodology to model the tradeoff for any substrate and encapsulant material. A surface charge density-based partial discharge inception voltage (PDIV) model was developed to overcome the challenges in electric field-based models. The model consists of a closed-form equation that accepts finite element simulation results as input and produces PDIV as output. The model was experimentally validated for various trace gaps in a 12/25/12 mil alumina DBC using Dow Corning 3-6635 dielectric gel. An expected trend of diminishing returns was observed between PDIV benefit and trace-gap. This approach quantifies this diminishing returns point. To design for reliability, the maximum operating voltage must be limited to the PDIV. For a material set and manufacturing process, this methodology can be used to determine the voltage class and trace gap design rules.

A multi-class logistic regression algorithm to reliably infer network connectivity from cell membrane potentials

In neuroscience, the structural connectivity matrix of synaptic weights between neurons is one of the critical factors that determine the overall function of a network of neurons. The mechanisms of signal transduction have been intensively studied at different time and spatial scales and both the cellular and molecular levels. While a better understanding and knowledge of some basic processes of information handling by neurons has been achieved, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor the electrical activity of a large number of neurons in real time. The analysis of the data related to the activities of individual neurons can become a very valuable tool for the study of the dynamics and architecture of neural networks. In particular, advances in optical imaging techniques allow us to record up to thousands of neurons nowadays. However, most of the efforts have been focused on calcium signals, that lack relevant aspects of cell activity. In recent years, progresses in the field of genetically encoded voltage indicators have shown that imaging signals could be well suited to record spiking and synaptic events from a large population of neurons. Here, we present a methodology to infer the connectivity of a population of neurons from their voltage traces. At first, putative synaptic events were detected. Then, a multi-class logistic regression was used to fit the putative events to the spiking activities and a penalization term was allowed to regulate the sparseness of the inferred network. The proposed Multi-Class Logistic Regression with L1 penalization (MCLRL) was benchmarked against data obtained from in silico network simulations. MCLRL properly inferred the connectivity of all tested networks, as indicated by the Matthew correlation coefficient (MCC). Importantly, MCLRL was accomplished to reconstruct the connectivity among subgroups of neurons sampled from the network. The robustness of MCLRL to noise was also assessed and the performances remained high (MCC>0.95) even in extremely high noise conditions (>95% noisy events). Finally, we devised a procedure to determine the optimal MCLRL regularization term, which allows us to envision its application to experimental data.

Development of a 50-A heaterless hollow cathode for electric thrusters.

Hollow cathodes in electric thrusters normally use an external heater to raise the thermionic electron emitter to emission temperatures. These heaters are a potential single-point failure in the thruster and add a separate power supply to the power processing unit. Heaterless hollow cathodes are attractive for their compact size and potential higher reliability but have only been reliably demonstrated to date in small hollow cathodes capable of discharge currents below around 5A. A new heaterless LaB6 hollow cathode has been developed that is capable of discharge currents from 5 to 50A. The cathode configuration extends the gas feed tube at cathode potential part way into the emitting insert region of the cathode. A high-voltage Paschen discharge is struck from the tube to the keeper that heats the tube tip, which then efficiently heats the insert by radiation. This configuration eliminates the arcing observed in prior large heaterless designs that coupled the high-voltage Paschen discharge to the orifice plate or the insert itself. Discharge current-voltage characteristics show that the presence of the tube does not significantly perturb the insert-region plasma. Startup uses a simple 3minignition procedure, and voltage traces of the keeper discharge reveal that much of the present tube-radiator's 100-to-150W heating power comes from an intermediate thermionic discharge sustained by the tube during the transition between the Paschen discharge and LaB6 insert thermionic regime. This novel heating mechanism enables an unprecedented class of higher-current heaterless hollow cathodes for the next generation of high-power electric propulsion systems.

HVDC grid fault location method using genetic algorithm on reconstructed frequency-domain voltage profiles

This paper presents a novel frequency domain based fault location method for High Voltage Direct Current (HVDC) grids. The method relies only on short data windows (below two milliseconds) of single-ended voltage measurements, thus rendering the technique practically applicable to HVDC grids employing high-speed circuit breakers. Based on the available post-fault voltage traces, a frequency domain profile of voltage transfer function is initially constructed, which is then iteratively evaluated against theoretical voltage profiles, synthesized with the use of travelling wave principles. The evaluation routine is based on a genetic algorithm (GA) regime, which once terminated indicates a close approximation to the feeder’s fault location. The result of this process is further refined using a weighted averaging function to obtain the final fault distance estimation. The performance of the proposed method is demonstrated using comprehensive electromagnetic transient (EMT) simulation studies conducted on a four-terminal meshed HVDC grid using PSCAD/EMTDC. The results revealed that the method can estimate the fault location with high accuracy for all transmission media types, including hybrid lines that comprise of cable and overhead line segments. Moreover, it has been found that the method maintains its accuracy for very long transmission lines, highly-resistive faults and different fault types.

A Simple Model of Nonspiking Neurons.

Due to the ubiquity of spiking neurons in neuronal processes, various simple spiking neuron models have been proposed as an alternative to conductance-based models (a.k.a. Hodgkin-Huxley-type models), known to be computationally expensive and difficult to treat mathematically. However, to the best of our knowledge, there is no equivalent in the literature of a simple and lightweight model for describing the voltage behavior of nonspiking neurons, which also are ubiquitous in a large variety of nervous tissues in both vertebrate and invertebrate species and play a central role in information processing. This letter proposes a simple model that reproduces the experimental qualitative behavior of known types of nonspiking neurons. The proposed model, which differs fundamentally from classic simple spiking models unable to characterize nonspiking dynamics due to their intrinsic structure, is derived from the bifurcation study of conductance-based models of nonspiking neurons. Since such neurons display a high sensitivity to noise, the model aims at capturing the experimental distribution of single-neuron responses rather than perfectly replicating a single given experimental voltage trace. We show that such a model can be used as a building block for realistic simulations of large nonspiking neuronal networks and is endowed with generalization capabilities, granted by design.

Non-linear and multi-domain modelling of a permanent magnet linear synchronous machine for free piston engine generators

• The motivations for continued FPE and LEM development are established. • A non-linear and multi-domain model of the PMLSM is presented. • Incorporation of the PMLSM in an FPEG multi-domain model is demonstrated. • Model validation with data captured from an experimental FPEG is conducted. • Simulation results indicate the model captures the dominant dynamics of the PMLSM. The Free Piston Engine (FPE) can be considered a viable and promising option in future low-carbon technology development. When coupled to the Linear Electric Machine (LEM) to produce electrical energy, its characteristic non-linear dynamics typically have been described as linear when considering a systems-level modelling approach. This paper presents a multi-domain model of a Permanent Magnet Linear Synchronous Machine (PMLSM) for Free Piston Engine Generators (FPEG) and offers a detailed non-linear mathematical description of the machine dynamics. The model was implemented in a free-piston expander system and validated against experimental data from a test rig that has identical parameters. The simulation results indicate a strong correlation to the experimental data, which captured the dominant dynamics of the PMLSM and prove the satisfactory accuracy and performance of the model, together with a similar voltage and current output trace, indicating a cyclic energy output error of approximately 10 %. This paper aims to extend the current knowledge and literature within FPEG PMLSM design by considering the inherent non-linearity and multi-directional nature of the system dynamics and its interactions with multi-physical domains.

Metal-wire-embedded alumina insulating material using micro- and nanoscale 3D printing for surface flashover mitigation in a vacuum

A micro- and nanoscale 3D printing technique is applied to fabricate a functional insulating material that mitigates surface discharge in a vacuum based on microscopic electron multipactor suppression. The proposed alumina ceramic insulator design consists of surface-embedded thin metal wires that introduce a local gradient of secondary electron emission (SEE) yield, such that the trajectories of multipactor electrons are distorted by accumulated negative surface charge and the SEE avalanche across the insulator surface becomes intermittent. Considerable increases of surface flashover threshold and surface charging reduction are verified by the experiment. Also, additional efforts are made to determine the optimal size and spatial distribution of the metal wire. A convex-shape flashover voltage trace is observed when increasing the width of the wire, suggesting a trade-off between the multipactor mitigation and the insulator strength. The wire’s position between the adjacent cathode triple junction and the middle of the insulator is proved to be favorable for flashover mitigation. The physical details of surface flashover mitigation by the proposed insulator design are revealed by an ab initio particle-in-cell simulation code, corroborating the experiment from a microscopic aspect.

Framework for automated sorting of neural spikes from Neuralynx-acquired tetrode recordings in freely-moving mice

BackgroundExtracellular recording represents a crucial electrophysiological technique in neuroscience for studying the activity of single neurons and neuronal populations. The electrodes capture voltage traces that, with the help of analytical tools, reveal action potentials (‘spikes’) as well as local field potentials. The process of spike sorting is used for the extraction of action potentials generated by individual neurons. Until recently, spike sorting was performed with manual techniques, which are laborious and unreliable due to inherent operator bias. As neuroscientists add multiple electrodes to their probes, the high-density devices can record hundreds to thousands of neurons simultaneously, making the manual spike sorting process increasingly difficult. The advent of automated spike sorting software has offered a compelling solution to this issue and, in this study, we present a simple-to-execute framework for running an automated spike sorter.MethodsTetrode recordings of freely-moving mice are obtained from the CA1 region of the hippocampus as they navigate a linear track. Tetrode recordings are also acquired from the prelimbic cortex, a region of the medial prefrontal cortex, while the mice are tested in a T maze. All animals are implanted with custom-designed, 3D-printed microdrives that carry 16 electrodes, which are bundled in a 4-tetrode geometry.ResultsWe provide an overview of a framework for analyzing single-unit data in which we have concatenated the acquisition system (Cheetah, Neuralynx) with analytical software (MATLAB) and an automated spike sorting pipeline (MountainSort). We give precise instructions on how to implement the different steps of the framework, as well as explanations of our design logic. We validate this framework by comparing manually-sorted spikes against automatically-sorted spikes, using neural recordings of the hippocampus and prelimbic cortex in freely-moving mice.ConclusionsWe have efficiently integrated the MountainSort spike sorter with Neuralynx-acquired neural recordings. Our framework is easy to implement and provides a high-throughput solution. We predict that within the broad field of bioelectronic medicine, those teams that incorporate high-density neural recording devices to their armamentarium might find our framework quite valuable as they expand their analytical footprint.