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Kramer, Mark A., Lauren M. Ostrowski, Daniel Y. Song, Emily L. Thorn, Sally M. Stoyell, McKenna Parnes, Dhinakaran Chinappen, et al. "Scalp recorded spike ripples predict seizure risk in childhood epilepsy better than spikes." Brain 142, no. 5 (March 25, 2019): 1296–309. http://dx.doi.org/10.1093/brain/awz059.
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Abstract In the past decade, brief bursts of fast oscillations in the ripple range have been identified in the scalp EEG as a promising non-invasive biomarker for epilepsy. However, investigation and clinical application of this biomarker have been limited because standard approaches to identify these brief, low amplitude events are difficult, time consuming, and subjective. Recent studies have demonstrated that ripples co-occurring with epileptiform discharges (‘spike ripple events’) are easier to detect than ripples alone and have greater pathological significance. Here, we used objective techniques to quantify spike ripples and test whether this biomarker predicts seizure risk in childhood epilepsy. We evaluated spike ripples in scalp EEG recordings from a prospective cohort of children with a self-limited epilepsy syndrome, benign epilepsy with centrotemporal spikes, and healthy control children. We compared the rate of spike ripples between children with epilepsy and healthy controls, and between children with epilepsy during periods of active disease (active, within 1 year of seizure) and after a period of sustained seizure-freedom (seizure-free, >1 year without seizure), using semi-automated and automated detection techniques. Spike ripple rate was higher in subjects with active epilepsy compared to healthy controls (P = 0.0018) or subjects with epilepsy who were seizure-free ON or OFF medication (P = 0.0018). Among epilepsy subjects with spike ripples, each month seizure-free decreased the odds of a spike ripple by a factor of 0.66 [95% confidence interval (0.47, 0.91), P = 0.021]. Comparing the diagnostic accuracy of the presence of at least one spike ripple versus a classic spike event to identify group, we found comparable sensitivity and negative predictive value, but greater specificity and positive predictive value of spike ripples compared to spikes (P = 0.016 and P = 0.006, respectively). We found qualitatively consistent results using a fully automated spike ripple detector, including comparison with an automated spike detector. We conclude that scalp spike ripple events identify disease and track with seizure risk in this epilepsy population, using both semi-automated and fully automated detection methods, and that this biomarker outperforms analysis of spikes alone in categorizing seizure risk. These data provide evidence that spike ripples are a specific non-invasive biomarker for seizure risk in benign epilepsy with centrotemporal spikes and support future work to evaluate the utility of this biomarker to guide medication trials and tapers in these children and predict seizure risk in other at-risk populations.
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Vemuru, Krishnamurthy V. "Implementation of the Canny Edge Detector Using a Spiking Neural Network." Future Internet 14, no. 12 (December 11, 2022): 371. http://dx.doi.org/10.3390/fi14120371.
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Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step of noise reduction using a Gaussian kernel and a final step to remove the weak edges by the hysteresis threshold. In this work, a spike-based computing algorithm is presented as a neuromorphic analogue of the Canny edge detector, where the five steps of the conventional algorithm are processed using spikes. A spiking neural network layer consisting of a simplified version of a conductance-based Hodgkin–Huxley neuron as a building block is used to calculate the gradients. The effectiveness of the spiking neural-network-based algorithm is demonstrated on a variety of images, showing its successful adaptation of the principle of the Canny edge detector. These results demonstrate that the proposed algorithm performs as a complete spike domain implementation of the Canny edge detector.
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Zhang, Dongmao, Karim N. Jallad, and Dor Ben-Amotz. "Stripping of Cosmic Spike Spectral Artifacts Using a New Upper-Bound Spectrum Algorithm." Applied Spectroscopy 55, no. 11 (November 2001): 1523–31. http://dx.doi.org/10.1366/0003702011953757.
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A new upper-bound spectrum (UBS) method for removal of cosmic spike artifacts from spectra or images collected using a charge-coupled device (CCD) detector is proposed. This algorithm, which is shown to outperform previous methods, relies on an upper-bound spectrum, derived from scaled copies of consecutively collected spectra, which serves as a threshold for the detection of suspected cosmic spikes. Detected spikes are removed by replacement with the corresponding points in other spectra. Thus, unlike other commonly used methods, the UBS algorithm requires no smoothing or noise filtering and more reliably removes cosmic spikes of all magnitudes while introducing far less (if any) spectral distortion. The UBS method is tested using both synthetic and experimental (gypsum and gypsum/hematite mixture) spectra containing variable background (fluorescence), noise, and cosmic spike interference. The UBS method may in rare instances mistakenly identify spectral noise or photo-induced changes in band intensity (or shape) as cosmic spikes. However, as demonstrated through the use of both synthetic and experimental examples, such misidentifications produce little or no spectral distortion or artifacts in the resulting cosmic-spike-free output spectra.
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Zhao, Jianqing, Xiaohu Zhang, Jiawei Yan, Xiaolei Qiu, Xia Yao, Yongchao Tian, Yan Zhu, and Weixing Cao. "A Wheat Spike Detection Method in UAV Images Based on Improved YOLOv5." Remote Sensing 13, no. 16 (August 5, 2021): 3095. http://dx.doi.org/10.3390/rs13163095.
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Deep-learning-based object detection algorithms have significantly improved the performance of wheat spike detection. However, UAV images crowned with small-sized, highly dense, and overlapping spikes cause the accuracy to decrease for detection. This paper proposes an improved YOLOv5 (You Look Only Once)-based method to detect wheat spikes accurately in UAV images and solve spike error detection and miss detection caused by occlusion conditions. The proposed method introduces data cleaning and data augmentation to improve the generalization ability of the detection network. The network is rebuilt by adding a microscale detection layer, setting prior anchor boxes, and adapting the confidence loss function of the detection layer based on the IoU (Intersection over Union). These refinements improve the feature extraction for small-sized wheat spikes and lead to better detection accuracy. With the confidence weights, the detection boxes in multiresolution images are fused to increase the accuracy under occlusion conditions. The result shows that the proposed method is better than the existing object detection algorithms, such as Faster RCNN, Single Shot MultiBox Detector (SSD), RetinaNet, and standard YOLOv5. The average accuracy (AP) of wheat spike detection in UAV images is 94.1%, which is 10.8% higher than the standard YOLOv5. Thus, the proposed method is a practical way to handle the spike detection in complex field scenarios and provide technical references for field-level wheat phenotype monitoring.
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Arai, Itaru, Yoshiyuki Yamada, Tomomitsu Asaka, and Masao Tachibana. "Light-Evoked Oscillatory Discharges in Retinal Ganglion Cells Are Generated by Rhythmic Synaptic Inputs." Journal of Neurophysiology 92, no. 2 (August 2004): 715–25. http://dx.doi.org/10.1152/jn.00159.2004.
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In the visual system, optimal light stimulation sometimes generates γ-range (ca. 20 ∼ 80 Hz) synchronous oscillatory spike discharges. This phenomenon is assumed to be related to perceptual integration. Applying a planar multi-electrode array to the isolated frog retina, Ishikane et al. demonstrated that dimming detectors, off-sustained type ganglion cells, generate synchronous oscillatory spike discharges in response to diffuse dimming illumination. In the present study, applying the whole cell current-clamp technique to the isolated frog retina, we examined how light-evoked oscillatory spike discharges were generated in dimming detectors. Light-evoked oscillatory (∼30 Hz) spike discharges were triggered by rhythmic (∼30 Hz) fluctuations superimposed on a depolarizing plateau potential. When a suprathreshold steady depolarizing current was injected into a dimming detector, only a few spikes were evoked at the stimulus onset. However, repetitive spikes were triggered by a γ-range sinusoidal current superimposed on the steady depolarizing current. Thus the light-evoked rhythmic fluctuations are likely to be generated presynaptically. The light-evoked rhythmic fluctuations were suppressed not by intracellular application of N-(2,6-dimethyl-phenylcarbamoylmethyl)triethylammonium bromide (QX-314), a Na+ channel blocker, to the whole cell clamped dimming detector but by bath-application of tetrodotoxin to the retina. The light-evoked rhythmic fluctuations were suppressed by a GABAA receptor antagonist but potentiated by a GABAC receptor antagonist, whereas these fluctuations were little affected by a glycine receptor antagonist. Because amacrine cells are spiking neurons and because GABA is one of the main transmitters released from amacrine cells, amacrine cells may participate in generating rhythmically fluctuated synaptic input to dimming detectors.
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Vallicelli, Elia, Marco Reato, Marta Maschietto, Stefano Vassanelli, Daniele Guarrera, Federico Rocchi, Gianmaria Collazuol, Ralf Zeitler, Andrea Baschirotto, and Marcello De Matteis. "Neural Spike Digital Detector on FPGA." Electronics 7, no. 12 (December 5, 2018): 392. http://dx.doi.org/10.3390/electronics7120392.
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This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are characterized by low signal-to-noise ratios caused by both the contactless sensing of weak extracellular voltages and the high noise power coming from cells and analog electronics signal processing. This low SNR forces to utilize advanced noise rejection algorithms to separate relevant neural activity from noise, which are usually implemented via software/off-line. However, off-line detection of neural spikes cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA-based system capable to detect in real-time neural spikes from background noise. The output signals of the proposed system provide real-time spatial and temporal information about the culture electrical activity and the noise power distribution with a minimum latency of 165 ns. The output bit-stream can be further utilized to detect synchronous activity within the neural network.
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Lüdtke, Niklas, and Mark E. Nelson. "Short-Term Synaptic Plasticity Can Enhance Weak Signal Detectability in Nonrenewal Spike Trains." Neural Computation 18, no. 12 (December 2006): 2879–916. http://dx.doi.org/10.1162/neco.2006.18.12.2879.
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We study the encoding of weak signals in spike trains with interspike interval (ISI) correlations and the signals' subsequent detection in sensory neurons. Motivated by the observation of negative ISI correlations in auditory and electrosensory afferents, we assess the theoretical performance limits of an individual detector neuron receiving a weak signal distributed across multiple afferent inputs. We assess the functional role of ISI correlations in the detection process using statistical detection theory and derive two sequential likelihood ratio detector models: one for afferents with renewal statistics; the other for afferents with negatively correlated ISIs. We suggest a mechanism that might enable sensory neurons to implicitly compute conditional probabilities of presynaptic spikes by means of short-term synaptic plasticity. We demonstrate how this mechanism can enhance a postsynaptic neuron's sensitivity to weak signals by exploiting the correlation structure of the input spike trains. Our model not only captures fundamental aspects of early electrosensory signal processing in weakly electric fish, but may also bear relevance to the mammalian auditory system and other sensory modalities.
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Kreuz, Thomas, Mario Mulansky, and Nebojsa Bozanic. "SPIKY: a graphical user interface for monitoring spike train synchrony." Journal of Neurophysiology 113, no. 9 (May 2015): 3432–45. http://dx.doi.org/10.1152/jn.00848.2014.
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Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.
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Ware, George M. "Method Validation Study of Hypoglycin A Determination in Ackee Fruit." Journal of AOAC INTERNATIONAL 85, no. 4 (July 1, 2002): 933–37. http://dx.doi.org/10.1093/jaoac/85.4.933.
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Abstract A study was conducted to validate the performance characteristics of a published method entitled “Reversed-Phase Liquid Chromatographic Detection of Hypoglycin A in Canned Ackee Fruit Sample.” Hypoglycin A (HG-A) was extracted from ackee fruit with 80% ethanol–water, centrifuged, and filtered; the sample extract then was reacted with phenylisothiocyanate. HG-A was separated by reversed-phase chromatography as the phenylthiocarbamyl derivative and detected at the low nanogram level using a UV detector at 254 nm. A study was conducted to determine recovery of HG-A added to a control ackee fruit sample. A control sample containing a low level of HG-A was spiked with 403.2, 201.6, 96.8, and 48.4 μg HG-A/g ackee fruit, respectively. Twelve replicates were analyzed for each spike level. The mean percent recovery ± standard deviation for spike levels 403.2, 201.6, 96.8, and 48.4 μg HG-A/g were 94.37 ± 1.27, 99.12 ± 2.09, 107.95 ± 5.42, and 129.18 ± 15.32%, respectively. The percent coefficient of variation (%CV) for spike levels 403.2, 201.6, 96.8, and 48.4 μg HG-A/g were 1.35, 2.11, 5.02, and 11.86%, respectively. The recovery data indicate that HG-A can be recovered from ackee fruit with excellent accuracy and precision. Precision data obtained from replicate assays of ackee fruit naturally contaminated with low, medium, and high HG-A levels is presented.
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Barker, Peter D. R. "Sensitization and multiplicative noise in the descending contralateral movement detector (DCMD) of the locust." Visual Neuroscience 10, no. 5 (September 1993): 791–809. http://dx.doi.org/10.1017/s0952523800006040.
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AbstractSpike discharges from the descending contralateral movement detector (DCMD) were recorded extracellularly from the ventral nerve cord of the locust in complete darkness, in response to dim flashes of constant-intensity light, and in response to pairs of identical flashes presented different intervals apart. Three phenomena were discovered: novel long-term sensitization which changes the DCMD's sensitivity to light, a multiplicative cascade process driven by shot events, and the suppression of the spike discharge shortly after a dim flash.The DCMD's spike discharge is stochastic. It can be considered as a two-stage cascade process producing intrinsic multiplicative noise. An effective photon, or thermal isomerization in complete darkness, produces an unseen shot event which in turn initiates a random number of DCMD spikes in a cluster. A shot initiates a variable number of spikes when it directs the rate of a Poisson process. The results of statistical analyses are consistent with this model when the amplitudes of shot events are variable. The transmission efficiency is low because at least 2.4–9.6 quantum bumps are required to produce one extra DCMD spike.The DCMD has a constant mean discharge rate of 0.25–1.5 spikes/s in complete darkness. Clustering about particular points in time (shots) leads to a lack of independence between interspike intervals, and the overdispersion of interspike interval and number distributions compared with those from a simple Poisson process. The mean cluster size is 1.3–1.6 spikes in darkness. Similar clustering was found in response to flashes of light.A dim flash changes the DCMD's sensitivity to light, even at threshold when no spike discharge results. Sensitization occurs because the average number of shot events produced by isoquantal flashes depends on the history of visual stimulation. This contributes to the nonlinear response-intensity function. The evolution of sensitization is roughly constant in different DCMD cells, lasting approximately 3 s after a flash. Sensitization was observed in response to light only, presumably because the intensity of dark-light is too low. It is proposed that sensitization is associated with a set of processes or molecular state in the presynaptic region of a chemical synapse.
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Kim, Jong, Hankyu Lee, and Hyoungho Ko. "0.6 V, 116 nW Neural Spike Acquisition IC with Self-Biased Instrumentation Amplifier and Analog Spike Extraction." Sensors 18, no. 8 (July 30, 2018): 2460. http://dx.doi.org/10.3390/s18082460.
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This paper presents an ultralow power 0.6 V 116 nW neural spike acquisition integrated circuit with analog spike extraction. To reduce power consumption, an ultralow power self-biased current-balanced instrumentation amplifier (IA) is proposed. The passive RC lowpass filter in the amplifier acts as both DC servo loop and self-bias circuit. The spike detector, based on an analog nonlinear energy operator consisting of a low-voltage open-loop differentiator and an open-loop gate-bulk input multiplier, is designed to emphasize the high frequency spike components nonlinearly. To reduce the spike detection error, the adjacent spike merger is also proposed. The proposed circuit achieves a low IA current consumption of 46.4 nA at 0.6 V, noise efficiency factor (NEF) of 1.81, the bandwidth from 102 Hz to 1.94 kHz, the input referred noise of 9.37 μVrms, and overall power consumption of 116 nW at 0.6 V. The proposed circuit can be used in the ultralow power spike pulses acquisition applications, including the neurofeedback systems on peripheral nerves with low neuron density.
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Casson, Alexander J., and Esther Rodriguez-Villegas. "Utilising noise to improve an interictal spike detector." Journal of Neuroscience Methods 201, no. 1 (September 2011): 262–68. http://dx.doi.org/10.1016/j.jneumeth.2011.07.007.
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Foxe, M. P., I. M. Cameron, M. W. Cooper, D. A. Haas, J. C. Hayes, A. A. Kriss, L. S. Lidey, J. M. Mendez, A. M. Prinke, and R. A. Riedmann. "Radioxenon detector calibration spike production and delivery systems." Journal of Radioanalytical and Nuclear Chemistry 307, no. 3 (December 24, 2015): 2021–27. http://dx.doi.org/10.1007/s10967-015-4668-2.
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Yang, Chenhui, Byron Olson, and Jennie Si. "A Multiscale Correlation of Wavelet Coefficients Approach to Spike Detection." Neural Computation 23, no. 1 (January 2011): 215–50. http://dx.doi.org/10.1162/neco_a_00063.
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Extracellular chronic recordings have been used as important evidence in neuroscientific studies to unveil the fundamental neural network mechanisms in the brain. Spike detection is the first step in the analysis of recorded neural waveforms to decipher useful information and provide useful signals for brain-machine interface applications. The process of spike detection is to extract action potentials from the recordings, which are often compounded with noise from different sources. This study proposes a new detection algorithm that leverages a technique from wavelet-based image edge detection. It utilizes the correlation between wavelet coefficients at different sampling scales to create a robust spike detector. The algorithm has one tuning parameter, which potentially reduces the subjectivity of detection results. Both artificial benchmark data sets and real neural recordings are used to evaluate the detection performance of the proposed algorithm. Compared with other detection algorithms, the proposed method has a comparable or better detection performance. In this letter, we also demonstrate its potential for real-time implementation.
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Rossum, M. C. W. van. "A Novel Spike Distance." Neural Computation 13, no. 4 (April 1, 2001): 751–63. http://dx.doi.org/10.1162/089976601300014321.
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The discrimination between two spike trains is a fundamental problem for both experimentalists and the nervous system itself. We introduce a measure for the distance between two spike trains. The distance has a time constant as a parameter. Depending on this parameter, the distance interpolates between a coincidence detector and a rate difference counter. The dependence of the distance on noise is studied with an integrate-andfire model. For an intermediate range of the time constants, the distance depends linearly on the noise. This property can be used to determine the intrinsic noise of a neuron.
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Tambaro, Mattia, Marta Bisio, Marta Maschietto, Alessandro Leparulo, and Stefano Vassanelli. "FPGA Design Integration of a 32-Microelectrodes Low-Latency Spike Detector in a Commercial System for Intracortical Recordings." Digital 1, no. 1 (January 30, 2021): 34–53. http://dx.doi.org/10.3390/digital1010003.
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Numerous experiments require low latencies in the detection and processing of the neural brain activity to be feasible, in the order of a few milliseconds from action to reaction. In this paper, a design for sub-millisecond detection and communication of the spiking activity detected by an array of 32 intracortical microelectrodes is presented, exploiting the real-time processing provided by Field Programmable Gate Array (FPGA). The design is embedded in the commercially available RHS stimulation/recording controller from Intan Technologies, that allows recording intracortical signals and performing IntraCortical MicroStimulation (ICMS). The Spike Detector (SD) is based on the Smoothed Nonlinear Energy Operator (SNEO) and includes a novel approach to estimate an RMS-based firing-rate-independent threshold, that can be tuned to fine detect both the single Action Potential (AP) and Multi Unit Activity (MUA). A low-latency SD together with the ICMS capability, creates a powerful tool for Brain-Computer-Interface (BCI) closed-loop experiments relying on the neuronal activity-dependent stimulation. The design also includes: A third order Butterworth high-pass IIR filter and a Savitzky-Golay polynomial fitting; a privileged fast USB connection to stream the detected spikes to a host computer and a sub-milliseconds latency Universal Asynchronous Receiver-Transmitter (UART) protocol communication to send detections and receive ICMS triggers. The source code and the instruction of the project can be found on GitHub.
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Saggese, Gerardo, and Antonio Giuseppe Maria Strollo. "Low-Power Energy-Based Spike Detector ASIC for Implantable Multichannel BMIs." Electronics 11, no. 18 (September 16, 2022): 2943. http://dx.doi.org/10.3390/electronics11182943.
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Advances in microtechnology have enabled an exponential increase in the number of neurons that can be simultaneously recorded. To meet high-channel count and implantability demands, emerging applications require new methods for local real-time processing to reduce the data to transmit. Nonlinear energy operators are widely used to distinguish neural spikes from background noise featuring a good tradeoff between hardware resources and accuracy. However, they require an additional smoothing filter, which affects both area occupation and power dissipation. In this paper, we investigate a spike detector, based on a series of two nonlinear energy operators, and a simple and adaptive threshold, based on a three-point median operator. We show that our proposal provides good accuracy compared to other energy-based detectors on a synthetic dataset at different noise levels. Based on the proposed technique, a 1024-channel neural signal processor was designed in a 28 nm TSMC CMOS process by using latch-based static random-access memory (SRAM), demonstrating a total power consumption of 1.4 μW/ch and a silicon area occupation of 230 μm2/ch. These features, together with a comparison with the state of the art, demonstrate that our proposal constitutes an alternative for the development of next-generation multichannel neural interfaces.
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Vemuru, Krishnamurthy V. "Image Edge Detector with Gabor Type Filters Using a Spiking Neural Network of Biologically Inspired Neurons." Algorithms 13, no. 7 (July 9, 2020): 165. http://dx.doi.org/10.3390/a13070165.
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We report the design of a Spiking Neural Network (SNN) edge detector with biologically inspired neurons that has a conceptual similarity with both Hodgkin-Huxley (HH) model neurons and Leaky Integrate-and-Fire (LIF) neurons. The computation of the membrane potential, which is used to determine the occurrence or absence of spike events, at each time step, is carried out by using the analytical solution to a simplified version of the HH neuron model. We find that the SNN based edge detector detects more edge pixels in images than those obtained by a Sobel edge detector. We designed a pipeline for image classification with a low-exposure frame simulation layer, SNN edge detection layers as pre-processing layers and a Convolutional Neural Network (CNN) as a classification module. We tested this pipeline for the task of classification with the Digits dataset, which is available in MATLAB. We find that the SNN based edge detection layer increases the image classification accuracy at lower exposure times, that is, for 1 < t < T /4, where t is the number of milliseconds in a simulated exposure frame and T is the total exposure time, with reference to a Sobel edge or Canny edge detection layer in the pipeline. These results pave the way for developing novel cognitive neuromorphic computing architectures for millisecond timescale detection and object classification applications using event or spike cameras.
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Li, H., and Q. Xu. "Sub-threshold-based ultra-low-power neural spike detector." Electronics Letters 47, no. 6 (2011): 367. http://dx.doi.org/10.1049/el.2010.3711.
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Kempter, Richard, Wulfram Gerstner, J. Leo van Hemmen, and Hermann Wagner. "Extracting Oscillations: Neuronal Coincidence Detection with Noisy Periodic Spike Input." Neural Computation 10, no. 8 (November 1, 1998): 1987–2017. http://dx.doi.org/10.1162/089976698300016945.
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How does a neuron vary its mean output firing rate if the input changes from random to oscillatory coherent but noisy activity? What are the critical parameters of the neuronal dynamics and input statistics? To answer these questions, we investigate the coincidence-detection properties of an integrate-and-fire neuron. We derive an expression indicating how coincidence detection depends on neuronal parameters. Specifically, we show how coincidence detection depends on the shape of the postsynaptic response function, the number of synapses, and the input statistics, and we demonstrate that there is an optimal threshold. Our considerations can be used to predict from neuronal parameters whether and to what extent a neuron can act as a coincidence detector and thus can convert a temporal code into a rate code.
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Giannakis, G. B., J. M. Mendel, and X. Zhao. "A fast prediction-error detector for estimating sparse-spike sequences." IEEE Transactions on Geoscience and Remote Sensing 27, no. 3 (May 1989): 344–51. http://dx.doi.org/10.1109/36.17677.
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Ashida, Go, Kousuke Abe, Kazuo Funabiki, and Masakazu Konishi. "Passive Soma Facilitates Submillisecond Coincidence Detection in the Owl's Auditory System." Journal of Neurophysiology 97, no. 3 (March 2007): 2267–82. http://dx.doi.org/10.1152/jn.00399.2006.
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Neurons of the avian nucleus laminaris (NL) compute the interaural time difference (ITD) by detecting coincident arrivals of binaural signals with submillisecond accuracy. The cellular mechanisms for this temporal precision have long been studied theoretically and experimentally. The myelinated axon initial segment in the owl's NL neuron and small somatic spikes observed in auditory coincidence detector neurons of various animals suggest that spikes in the NL neuron are generated at the first node of Ranvier and that the soma passively receives back-propagating spikes. To investigate the significance of the “passive soma” structure, we constructed a two-compartment NL neuron model, consisting of a cell body and a first node, and systematically changed the excitability of each compartment. Here, we show that a neuron with a less active soma achieves higher ITD sensitivity and higher noise tolerance with lower energy costs. We also investigate the biophysical mechanism of the computational advantage of the “passive soma” structure by performing sub- and suprathreshold analyses. Setting a spike initiation site with high sodium conductance, not in the large soma but in the small node, serves to amplify high-frequency input signals and to reduce the impact and the energy cost of spike generation. Our results indicate that the owl's NL neuron uses a “passive soma” design for computational and metabolic reasons.
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Rubin, Jonathan E., Richard C. Gerkin, Guo-Qiang Bi, and Carson C. Chow. "Calcium Time Course as a Signal for Spike-Timing–Dependent Plasticity." Journal of Neurophysiology 93, no. 5 (May 2005): 2600–2613. http://dx.doi.org/10.1152/jn.00803.2004.
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Calcium has been proposed as a postsynaptic signal underlying synaptic spike-timing–dependent plasticity (STDP). We examine this hypothesis with computational modeling based on experimental results from hippocampal cultures, some of which are presented here, in which pairs and triplets of pre- and postsynaptic spikes induce potentiation and depression in a temporally asymmetric way. Specifically, we present a set of model biochemical detectors, based on plausible molecular pathways, which make direct use of the time course of the calcium signal to reproduce these experimental STDP results. Our model features a modular structure, in which long-term potentiation (LTP) and depression (LTD) components compete to determine final plasticity outcomes; one aspect of this competition is a veto through which appropriate calcium time courses suppress LTD. Simulations of our model are also shown to be consistent with classical LTP and LTD induced by several presynaptic stimulation paradigms. Overall, our results provide computational evidence that, while the postsynaptic calcium time course contains sufficient information to distinguish various experimental long-term plasticity paradigms, small changes in the properties of back-propagation of action potentials or in synaptic dynamics can alter the calcium time course in ways that will significantly affect STDP induction by any detector based exclusively on postsynaptic calcium. This may account for the variability of STDP outcomes seen within hippocampal cultures, under repeated application of a single experimental protocol, as well as for that seen in multiple spike experiments across different systems.
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Yuan, Mingzheng. "An Absolute-value Detector with Threshold Comparing for Spike Detection in Brain-machine Interface." Journal of Physics: Conference Series 2113, no. 1 (November 1, 2021): 012038. http://dx.doi.org/10.1088/1742-6596/2113/1/012038.
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Abstract This research designs an absolute-value detector with the function of threshold comparing. Specifically, it is an essential device in the spike detection of the brain-machine interface. The optimized design in the research can accomplish the main functions in spike detection and has good performance in both delay and energy consumption. It comes up with two types of design at the beginning. To make the design reliable and comprehensive, it decides to discuss both methods in this paper. The first design is using a full adder, multiplexer and comparator. The concept of its logic circuit is adding the logic one to the input when the given input data is negative, keeping the original information as the given input data is positive. To achieve the function of adding, this study chooses the full adders. The primary purpose of using multiplexers is to select from the processed input and original input, and the choice depends on the most significant bit (MSB) of the input data. To compare the absolute value of the input data with a given threshold, this research used a multi-bit comparator. The second design is based on the fundamental algorithms of calculating total numbers. It indicates that this study can operate it with the threshold value through a subtractor when the input is negative. On the contrary, an adder can be used when the information is positive. Based on the concept of logic optimization, this study chooses to use the only subtractors, and it just needs to focus on the borrow bit, which can indicate the more significant number. By connecting the MSB of the input with the subtractors through XOR gates, the selection can be achieved without using any multiplexer. In the process of removing and replacing the devices, it reached the optimization of the design. Then, this paper compared the minimum delay by calculating each stage’s size and finding that the second design is better. Finally, based on the dual design, this essay computed the energy consumption in the circuit and implement VDD optimization to obtain the minimum energy.
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Masquelier, Timothée. "STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons." Neuroscience 389 (October 2018): 133–40. http://dx.doi.org/10.1016/j.neuroscience.2017.06.032.
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Karmarkar, Uma R., and Dean V. Buonomano. "A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors?" Journal of Neurophysiology 88, no. 1 (July 1, 2002): 507–13. http://dx.doi.org/10.1152/jn.2002.88.1.507.
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In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the “standard model” of plasticity in which moderate increases in Ca2+ influx through the N-methyl-d-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca2+ influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca2+, which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors.
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Kenyon, Garrett T., James Theiler, John S. George, Bryan J. Travis, and David W. Marshak. "Correlated Firing Improves Stimulus Discrimination in a Retinal Model." Neural Computation 16, no. 11 (November 1, 2004): 2261–91. http://dx.doi.org/10.1162/0899766041941916.
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Synchronous firing limits the amount of information that can be extracted by averaging the firing rates of similarly tuned neurons. Here, we show that the loss of such rate-coded information due to synchronous oscillations between retinal ganglion cells can be overcome by exploiting the information encoded by the correlations themselves. Two very different models, one based on axon-mediated inhibitory feedback and the other on oscillatory common input, were used to generate artificial spike trains whose synchronous oscillations were similar to those measured experimentally. Pooled spike trains were summed into a threshold detector whose output was classified using Bayesian discrimination. For a threshold detector with short summation times, realistic oscillatory input yielded superior discrimination of stimulus intensity compared to rate-matched Poisson controls. Even for summation times too long to resolve synchronous inputs, gamma band oscillations still contributed to improved discrimination by reducing the total spike count variability, or Fano factor. In separate experiments in which neurons were synchronized in a stimulus-dependent manner without attendant oscillations, the Fano factor increased markedly with stimulus intensity, implying that stimulus-dependent oscillations can offset the increased variability due to synchrony alone.
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Yang, Yuning, C. Sam Boling, Awais M. Kamboh, and Andrew J. Mason. "Adaptive Threshold Neural Spike Detector Using Stationary Wavelet Transform in CMOS." IEEE Transactions on Neural Systems and Rehabilitation Engineering 23, no. 6 (November 2015): 946–55. http://dx.doi.org/10.1109/tnsre.2015.2425736.
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Bergman, Hagai, and Mahlon R. DeLong. "A personal computer-based spike detector and sorter: implementation and evaluation." Journal of Neuroscience Methods 41, no. 3 (March 1992): 187–97. http://dx.doi.org/10.1016/0165-0270(92)90084-q.
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Zhang, Yixin, Fei Xie, Guowen Yang, Xuping Zhang, and Shun Wang. "Balanced Single Photon Avalanche Detector with Variode-Based Spike Noise Cancellation." Microwave and Optical Technology Letters 55, no. 12 (September 23, 2013): 2877–79. http://dx.doi.org/10.1002/mop.27961.
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Lee, Samiuela, Cheang S. Khoo, Jarryd L. Pearson, James R. Hennell, and Alan Bensoussan. "Liquid Chromatographic Determination of Narirutin and Hesperidin in Zhi Ke (Citrus aurantium L.) in the Form of the Raw Herb and of the Dried Aqueous Extract." Journal of AOAC INTERNATIONAL 92, no. 3 (May 1, 2009): 789–96. http://dx.doi.org/10.1093/jaoac/92.3.789.
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Abstract A validated analytical method is reported for the analysis of narirutin and hesperidin in Zhi Ke (Citrus aurantium L.) in the form of the dried raw herb and of the commercially prepared dried aqueous extract. The samples were extracted by sonication in methanol and the extract was analyzed by liquid chromatography-photodiode array (PDA) detection with identity confirmation by negative electrospray ionization-tandem mass spectrometry (MS). A C18 column was used with a methanolwater gradient mobile phase. Narirutin and hesperidin were quantified at 284 nm using the PDA detector. Using the MS detector, the narirutin precursor ion with m/z 579 produced daughter ions with m/z 271 and 151. For hesperidin, the precursor ion with m/z 609 produced the m/z 301, 285, and 164 ions. The amounts of narirutin and hesperidin found in the certified raw herb were 14.2 and 147.9 mg/g, respectively, and in the dried aqueous extract the amounts were 9.2 and 8.6 mg/g, respectively. For the raw herb, the average recovery across the three spike levels (50, 100, and 150) for narirutin and hesperidin were 110.7 and 94.5, respectively. For the dried aqueous extract, the average recovery across the three spike levels for narirutin and hesperidin were 85.8 and 98.9, respectively.
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Makanda, Ulrich, Alexandre Voinot, Ramjee Kandel, Yu Wu, Matthew Leybourne, and Peng Wang. "Purity analysis for room-temperature semiconductor radiation detection material, CsPbBr3, using ICP-MS." Journal of Analytical Atomic Spectrometry 35, no. 11 (2020): 2672–78. http://dx.doi.org/10.1039/d0ja00223b.
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An ICP-MS protocol has been adapted to the impurity analysis of potential radiation detector, CsPbBr3. The newly developed method was validated by conducting a series of spike-and-recovery experiments based on solution synthesized CsPbBr3.
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Sobolev, Victor S., Bruce W. Horn, Joe W. Dorner, and Richard J. Cole. "Liquid Chromatographic Determination of Major Secondary Metabolites Produced by Aspergillus Species from Section Flavi." Journal of AOAC INTERNATIONAL 81, no. 1 (January 1, 1998): 57–60. http://dx.doi.org/10.1093/jaoac/81.1.57.
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Abstract A liquid chromatographic (LC) method was developed for simultaneous determination of major secondary metabolites—including cyclopiazonic acid (CPA), O-methylsterigmatocystin (OMST), and the versicolorins—produced by Aspergillus species from section Flavi (A. flavus, A. parasiticus, A. tamarii, and A. caelatus) on a liquid medium. Metabolites were extracted with chloroform and quantitated without prior cleanup by means of normal- phase ion-pair partition LC on silica gel with a mobile phase of n-heptane-2-propanol-n-butanolwater- tetrabutylammonium hydroxide (2560 + 900 + 230 + 32 + 8, v/v). Recoveries of CPA and OMST from fungal cultures spiked at 10 μg/mL were 98.90 + 3.27 and 95.92 + 5.27% (n = 5), respectively. At spike levels of 100 μg/mL, recoveries were 98.89 + 3.87 and 97.65 + 4.32% (n = 5), respectively. Limits of detection for pure standards were 0.25 μg/mL for CPA (at 280 nm) and 0.30 μg/mL for OMST (at 310 nm). UV detector responses to CPA and OMST were linear to about 0.5 and 3.5 μg/injection, respectively
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Dwivedi, Shashank, and Anup K. Gogoi. "A novel adaptive real-time detection algorithm for an area-efficient CMOS spike detector circuit." AEU - International Journal of Electronics and Communications 88 (May 2018): 87–97. http://dx.doi.org/10.1016/j.aeue.2018.02.023.
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Panitz, J. C., F. Zimmermann, F. Fischer, W. Häfner, and A. Wokaun. "Near-Infrared Raman Spectroscopy with High Resolution Using the Scanning Multichannel Technique." Applied Spectroscopy 48, no. 4 (April 1994): 454–57. http://dx.doi.org/10.1366/000370294775268884.
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An experimental setup has been developed for the measurement of Raman spectra with NIR excitation, which combines high resolution with multichannel detection. The instrument is based on a Ti:sapphire laser for excitation, a double monochromator, and a CCD detector. The scanning multichannel technique is used for efficient acquisition of Raman spectra. Principal features of the software designed for control of the spectrometer are described, including definition of problem-adapted resolution elements and spike-removal routines. Raman spectra of several compounds are given, demonstrating the good resolution obtainable with this version of NIR Raman spectroscopy.
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Li, Hongge, Huixin Bai, Qicheng Xu, and Tongsheng Xia. "Low-power MicroVrms noise neural spike detector for implantable interface microsystem device." Microelectronics Reliability 55, no. 5 (April 2015): 807–14. http://dx.doi.org/10.1016/j.microrel.2015.02.001.
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Line, T. L., J. Park, T. George, and E. W. Jones. "Doping-spike LWIR PtSi Schottky IR detector fabricated by molecular beam epitaxy." IEEE Transactions on Electron Devices 40, no. 11 (1993): 2143. http://dx.doi.org/10.1109/16.239832.
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Czanner, Gabriela, Sonja Grün, and Satish Iyengar. "Theory of the Snowflake Plot and Its Relations to Higher-Order Analysis Methods." Neural Computation 17, no. 7 (July 1, 2005): 1456–79. http://dx.doi.org/10.1162/0899766053723041.
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The snowflake plot is a scatter plot that displays relative timings of three neurons. It has had rather limited use since its introduction by Perkel, Gerstein, Smith, and Tatton (1975), in part because its triangular coordinates are unfamiliar and its theoretical properties are not well studied. In this letter, we study certain quantitative properties of this plot: we use projections to relate the snowflake plot to the cross-correlation histogram and the spike-triggered joint histogram, study the sampling properties of the plot for the null case of independent spike trains, study a simulation of a coincidence detector, and describe the extension of this plot to more than three neurons.
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Li, He, Peng Wang, and Chong Huang. "Comparison of Deep Learning Methods for Detecting and Counting Sorghum Heads in UAV Imagery." Remote Sensing 14, no. 13 (June 30, 2022): 3143. http://dx.doi.org/10.3390/rs14133143.
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With the rapid development of remote sensing with small, lightweight unmanned aerial vehicles (UAV), efficient and accurate crop spike counting, and yield estimation methods based on deep learning (DL) methods have begun to emerge, greatly reducing labor costs and enabling fast and accurate counting of sorghum spikes. However, there has not been a systematic, comprehensive evaluation of their applicability in cereal crop spike identification in UAV images, especially in sorghum head counting. To this end, this paper conducts a comparative study of the performance of three common DL algorithms, EfficientDet, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLOv4), for sorghum head detection based on lightweight UAV remote sensing data. The paper explores the effects of overlap ratio, confidence, and intersection over union (IoU) parameters, using the evaluation metrics of precision P, recall R, average precision AP, F1 score, computational efficiency, and the number of detected positive/negative samples (Objects detected consistent/inconsistent with real samples). The experiment results show the following. (1) The detection results of the three methods under dense coverage conditions were better than those under medium and sparse conditions. YOLOv4 had the most accurate detection under different coverage conditions; on the contrary, EfficientDet was the worst. While SSD obtained better detection results under dense conditions, the number of over-detections was larger. (2) It was concluded that although EfficientDet had a good positive sample detection rate, it detected the fewest samples, had the smallest R and F1, and its actual precision was poor, while its training time, although medium, had the lowest detection efficiency, and the detection time per image was 2.82-times that of SSD. SSD had medium values for P, AP, and the number of detected samples, but had the highest training and detection efficiency. YOLOv4 detected the largest number of positive samples, and its values for R, AP, and F1 were the highest among the three methods. Although the training time was the slowest, the detection efficiency was better than EfficientDet. (3) With an increase in the overlap ratios, both positive and negative samples tended to increase, and when the threshold value was 0.3, all three methods had better detection results. With an increase in the confidence value, the number of positive and negative samples significantly decreased, and when the threshold value was 0.3, it balanced the numbers for sample detection and detection accuracy. An increase in IoU was accompanied by a gradual decrease in the number of positive samples and a gradual increase in the number of negative samples. When the threshold value was 0.3, better detection was achieved. The research findings can provide a methodological basis for accurately detecting and counting sorghum heads using UAV.
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Mikula, Shawn, and Ernst Niebur. "The Effects of Input Rate and Synchrony on a Coincidence Detector: Analytical Solution." Neural Computation 15, no. 3 (March 1, 2003): 539–47. http://dx.doi.org/10.1162/089976603321192068.
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We derive analytically the solution for the output rate of the ideal coincidence detector. The solution is for an arbitrary number of input spike trains with identical binomial count distributions (which includes Poisson statistics as a special case) and identical arbitrary pairwise cross-correlations, from zero correlation (independent processes) to complete correlation (identical processes).
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Ahmad, Nazir, Lin Guo, Peter Mandarakas, Sbmeone Appleby, and George Bugueno. "Passive Diffusion through Polymeric Membranes: A Novel Cleanup Procedure for Analysis of Azinphos-Methyl and Azinphos-Ethyl Residues in Fruits and Vegetables." Journal of AOAC INTERNATIONAL 78, no. 6 (November 1, 1995): 1450–54. http://dx.doi.org/10.1093/jaoac/78.6.1450.
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Abstract A novel procedure is described for simple removal of coextractives prior to analysis of fruits and vegetables for azinphos-methyl and azinphos-ethyl residues. The solvent extract is concentrated, placed in a polymeric membrane tube, and then dialyzed in cyclohexane. Both azinphos-methyl and azinphos- ethyl diffuse into the surrounding solvent while coextractants remain inside the membrane. The dialyzing solvent is exchanged during concentration with n-hexane and analyzed without further cleanup by gas-liquid chromatography with a specific thermionic detector. The detection limit for a 25 g grape sample with final volume of extract made to 15 mL was 0.01 mg/kg. Recoveries of both residues from grapes averaged 107% (spike levels of 0.3 to 2.0 mg/kg). From a 20 g spinach sample, recoveries averaged 82% for azinphos-methyl and 72% for azinphos-ethyl when final volume of extract was made to 5 mL (spike levels of 0.1 to 1.0 mg/kg). Recoveries from 20 types of fruits and vegetables (20 g sample spiked at 1 mg/kg for both azinphos-methyl and azinphos-ethyl) were consistently greater than 70%, except for strawberries (61–67%) and avocado (28–34%). The high lipid content of avocado may impede diffusion of azinphosmethyl and azinphos-ethyl through the polymeric membrane. A field evaluation of the procedure showed a strong correlation (r = 0.957) between azinphos-methyl residues on grapes and treatments with 2 spray formulations. The membrane cleanup procedure is a simple and cost-effective alternative to other column or liquid–liquid partitioning procedures for azinphos-methyl and azinphosethyl residue analysis.
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Judge, S., and F. Rind. "The locust DCMD, a movement-detecting neurone tightly tuned to collision trajectories." Journal of Experimental Biology 200, no. 16 (August 1, 1997): 2209–16. http://dx.doi.org/10.1242/jeb.200.16.2209.
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A Silicon Graphics computer was used to challenge the locust descending contralateral movement detector (DCMD) neurone with images of approaching objects. The DCMD gave its strongest response, measured as either total spike number or spike frequency, to objects approaching on a direct collision course. Deviation in either a horizontal or vertical direction from a direct collision course resulted in a reduced response. The decline in the DCMD response with increasing deviation from a collision course was used as a measure of the tightness of DCMD tuning for collision trajectories. Tuning was defined as the half-width of the response when it had fallen to half its maximum level. The response tuning, measured as averaged mean spike number versus deviation away from a collision course, had a half-width at half-maximum response of 2.4 °­3.0 ° for a deviation in the horizontal direction and 3.0 ° for a deviation in the vertical direction. Mean peak spike frequency showed an even sharper tuning, with a half-width at half-maximum response of 1.8 ° for deviations away from a collision course in the horizontal plane.
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Saggese, Gerardo, and Antonio Giuseppe Maria Strollo. "A Low Power 1024-Channels Spike Detector Using Latch-Based RAM for Real-Time Brain Silicon Interfaces." Electronics 10, no. 24 (December 9, 2021): 3068. http://dx.doi.org/10.3390/electronics10243068.
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High-density microelectrode arrays allow the neuroscientist to study a wider neurons population, however, this causes an increase of communication bandwidth. Given the limited resources available for an implantable silicon interface, an on-fly data reduction is mandatory to stay within the power/area constraints. This can be accomplished by implementing a spike detector aiming at sending only the useful information about spikes. We show that the novel non-linear energy operator called ASO in combination with a simple but robust noise estimate, achieves a good trade-off between performance and consumption. The features of the investigated technique make it a good candidate for implantable BMIs. Our proposal is tested both on synthetic and real datasets providing a good sensibility at low SNR. We also provide a 1024-channels VLSI implementation using a Random-Access Memory composed by latches to reduce as much as possible the power consumptions. The final architecture occupies an area of 2.3 mm2, dissipating 3.6 µW per channels. The comparison with the state of art shows that our proposal finds a place among other methods presented in literature, certifying its suitability for BMIs.
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Gutkin, Boris S., G. Bard Ermentrout, and Alex D. Reyes. "Phase-Response Curves Give the Responses of Neurons to Transient Inputs." Journal of Neurophysiology 94, no. 2 (August 2005): 1623–35. http://dx.doi.org/10.1152/jn.00359.2004.
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Neuronal firing is determined largely by incoming barrages of excitatory postsynaptic potentials (EPSPs), each of which produce a transient increase in firing probability. To measure the effects of weak transient inputs on firing probability of cortical neurons, we compute phase-response curves (PRCs). PRCs, whose shape can be related to the dynamics of spike generation, document the changes in timing of spikes caused by an EPSP in a repetitively firing neuron as a function of when it arrives in the interspike interval (ISI). The PRC can be exactly related to the poststimulus time histogram (PSTH) so that knowledge of one uniquely determines the other. Typically, PRCs have zero values at the start and end of the ISI, where EPSPs have minimal effects and a peak in the middle. Where the peak occurs depends in part on the firing properties of neurons. The PRC can have regions of positivity and negativity corresponding respectively to speeding up and slowing down the time of the next spike. A simple canonical model for spike generation is introduced that shows how both the background firing rate and the degree of postspike afterhyperpolarization contribute to the shape of the PRC and thus to the PSTH. PRCs in strongly adapting neurons are highly skewed to the right (indicating a higher change in probability when the EPSPs appear late in the ISI) and can have negative regions (indicating a decrease in firing probability) early in the ISI. The PRC becomes more skewed to the right as the firing rate decreases. Thus at low firing rates, the spikes are triggered preferentially by inputs that occur only during a small time interval late in the ISI. This implies that the neuron is more of a coincidence detector at low firing frequencies and more of an integrator at high frequencies. The steady-state theory is shown to also hold for slowly varying inputs.
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Lubejko, Susan T., Bertrand Fontaine, Sara E. Soueidan, and Katrina M. MacLeod. "Spike threshold adaptation diversifies neuronal operating modes in the auditory brain stem." Journal of Neurophysiology 122, no. 6 (December 1, 2019): 2576–90. http://dx.doi.org/10.1152/jn.00234.2019.
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Single neurons function along a spectrum of neuronal operating modes whose properties determine how the output firing activity is generated from synaptic input. The auditory brain stem contains a diversity of neurons, from pure coincidence detectors to pure integrators and those with intermediate properties. We investigated how intrinsic spike initiation mechanisms regulate neuronal operating mode in the avian cochlear nucleus. Although the neurons in one division of the avian cochlear nucleus, nucleus magnocellularis, have been studied in depth, the spike threshold dynamics of the tonically firing neurons of a second division of cochlear nucleus, nucleus angularis (NA), remained unexplained. The input-output functions of tonically firing NA neurons were interrogated with directly injected in vivo-like current stimuli during whole cell patch-clamp recordings in vitro. Increasing the amplitude of the noise fluctuations in the current stimulus enhanced the firing rates in one subset of tonically firing neurons (“differentiators”) but not another (“integrators”). We found that spike thresholds showed significantly greater adaptation and variability in the differentiator neurons. A leaky integrate-and-fire neuronal model with an adaptive spike initiation process derived from sodium channel dynamics was fit to the firing responses and could recapitulate >80% of the precise temporal firing across a range of fluctuation and mean current levels. Greater threshold adaptation explained the frequency-current curve changes due to a hyperpolarized shift in the effective adaptation voltage range and longer-lasting threshold adaptation in differentiators. The fine-tuning of the intrinsic properties of different NA neurons suggests they may have specialized roles in spectrotemporal processing. NEW & NOTEWORTHY Avian cochlear nucleus angularis (NA) neurons are responsible for encoding sound intensity for sound localization and spectrotemporal processing. An adaptive spike threshold mechanism fine-tunes a subset of repetitive-spiking neurons in NA to confer coincidence detector-like properties. A model based on sodium channel inactivation properties reproduced the activity via a hyperpolarized shift in adaptation conferring fluctuation sensitivity.
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Gabbiani, Fabrizio, and Holger G. Krapp. "Spike-Frequency Adaptation and Intrinsic Properties of an Identified, Looming-Sensitive Neuron." Journal of Neurophysiology 96, no. 6 (December 2006): 2951–62. http://dx.doi.org/10.1152/jn.00075.2006.
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We investigated in vivo the characteristics of spike-frequency adaptation and the intrinsic membrane properties of an identified, looming-sensitive interneuron of the locust optic lobe, the lobula giant movement detector (LGMD). The LGMD had an input resistance of 4–5 MΩ, a membrane time constant of about 8 ms, and exhibited inward rectification and rebound spiking after hyperpolarizing current pulses. Responses to depolarizing current pulses revealed the neuron's intrinsic bursting properties and pronounced spike-frequency adaptation. The characteristics of adaptation, including its time course, the attenuation of the firing rate, the mutual dependency of these two variables, and their dependency on injected current, closely followed the predictions of a model first proposed to describe the adaptation of cat visual cortex pyramidal neurons in vivo. Our results thus validate the model in an entirely different context and suggest that it might be applicable to a wide variety of neurons across species. Spike-frequency adaptation is likely to play an important role in tuning the LGMD and in shaping the variability of its responses to visual looming stimuli.
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Tong, Qi, Jian Zheng Song, and Qiu Rong Li. "Rapid Determination of Formaldehyde in Dried Bean Milk Cream by HPLC with Pre-Column Derivatization." Advanced Materials Research 554-556 (July 2012): 1493–97. http://dx.doi.org/10.4028/www.scientific.net/amr.554-556.1493.
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A high-performance liquid chromatography method was set up for rapid determination of formaldehyde in dried bean milk cream. This thesis studies the Nash derivatization, derivative solubility, reaction time, amounts of Nash and derivative stability. The derivative is chromatographic separated by Agilent Zorbax Eclipse XDB-C18 column (4.6mm× 250mm, 5μm) and detected by index detector with VWD (412nm). The heater does not need the temperature-controlling system. A mobile phase was composed of acetonitrile and water (50:50, V/V) at a flow rate of 0.8 mL/min. Under the conditions of the above-mentioned test, the developed calibration curves displayed good linearity over a concentration range of 0.00 to 0.80mg/L, with a correlation coefficient exceeding 0.9998. Average spike recovery was found in a range of 88% to 91%, with a relative standard deviation (RSD) between 1.1% and 4.0%.The minimum detection limit of source is 0.013mg/Kg. The method can be used for rapid test on formaldehyde preservative in dried bean milk cream.
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Bedenbaugh, Purvis, and George L. Gerstein. "Multiunit Normalized Cross Correlation Differs from the Average Single-Unit Normalized Correlation." Neural Computation 9, no. 6 (August 1, 1997): 1265–75. http://dx.doi.org/10.1162/neco.1997.9.6.1265.
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As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored. We calculated the normalized cross-correlation (NCC) between single unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster. Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.
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Levi, Rafael, Otar Akanyeti, Aleksander Ballo, and James C. Liao. "Frequency response properties of primary afferent neurons in the posterior lateral line system of larval zebrafish." Journal of Neurophysiology 113, no. 2 (January 15, 2015): 657–68. http://dx.doi.org/10.1152/jn.00414.2014.
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The ability of fishes to detect water flow with the neuromasts of their lateral line system depends on the physiology of afferent neurons as well as the hydrodynamic environment. Using larval zebrafish ( Danio rerio), we measured the basic response properties of primary afferent neurons to mechanical deflections of individual superficial neuromasts. We used two types of stimulation protocols. First, we used sine wave stimulation to characterize the response properties of the afferent neurons. The average frequency-response curve was flat across stimulation frequencies between 0 and 100 Hz, matching the filtering properties of a displacement detector. Spike rate increased asymptotically with frequency, and phase locking was maximal between 10 and 60 Hz. Second, we used pulse train stimulation to analyze the maximum spike rate capabilities. We found that afferent neurons could generate up to 80 spikes/s and could follow a pulse train stimulation rate of up to 40 pulses/s in a reliable and precise manner. Both sine wave and pulse stimulation protocols indicate that an afferent neuron can maintain their evoked activity for longer durations at low stimulation frequencies than at high frequencies. We found one type of afferent neuron based on spontaneous activity patterns and discovered a correlation between the level of spontaneous and evoked activity. Overall, our results establish the baseline response properties of lateral line primary afferent neurons in larval zebrafish, which is a crucial step in understanding how vertebrate mechanoreceptive systems sense and subsequently process information from the environment.
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Rahamouz-Haghighi, Samaneh, Khadijeh Bagheri, Neda Mohsen-Pour, and Ali Sharafi. "Quantitative Determination of Apigenin, Catalpol, and Gallic Acid in Total Extracts From Different Parts of Plantago Species by High-Performance Liquid Chromatography." Avicenna Journal of Pharmaceutical Research 2, no. 2 (December 30, 2021): 49–54. http://dx.doi.org/10.34172/ajpr.2021.10.
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Background: Plantago species have been used in traditional medicine to treat many types of diseases. The detection of apigenin, catalpol, and gallic acid in Plantago lanceolata and Plantago major has been optimized using this protocol. Methods: The analyses were optimized using the C8 column, acetonitrile, and orthophosphoric acid–water (1:1%) as mobile phase at a flow rate of 1 mL.min-1, and a wavelength detector was observed at λ 204 nm. Results: The limits of detection (LOD) and quantification (LOQ) of the method were "0.04 and 0.14 μg/ mL", "0.007 and 0.022 μg/mL", as well as "0.02 and 0.073 μg/mL" for catalpol, apigenin, and gallic acid, respectively. The highest level of apigenin in the dry weight of plants (4.34, and 1.99 μg/mg) was obtained from the spike and aerial parts of P. lanceolata and P. major species. High levels of gallic acid extracted from aerial parts and leaves of both species were 12.85 and 10.11 μg/mg, respectively. The highest amount of catalpol (43.33 and 18.15 μg/mg DW) was obtained from the spike of both Plantago sp. The calibration curves were linear with a correlation coefficient (r>0.9991, 0.9996, and 0.9978). Conclusion: In sum, the most simple and sensitive method to measure compounds was developed using HPLC, which showed a great validity.