Note on the coefficient of variations of neuronal spike trains

Lengler, Johannes; Steger, Angelika

doi:10.1007/s00422-017-0717-y

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…The results of ANOVA demonstrated that the extraction time and extraction power (BD) had a significant (p < 0.05) negative effect on the comprehensive evaluation value ( Table 1). The coefficient of variation (CV) is defined as the quotient of standard deviation and mean value [36,37]. CV was used for analyzing the statistical data.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous Optimization of the Ultrasonic Extraction Method and Determination of the Antioxidant Activities of Hydroxysafflor Yellow A and Anhydrosafflor Yellow B from Safflower Using a Response Surface Methodology

Zhang

Jin

et al. 2020

Molecules

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An evaluation of the ultrasonic extraction process and the antioxidant activities of hydroxysafflor yellow A (HSYA) and anhydrosafflor yellow B (AHSYB) from safflower are presented herein. Using response surface methodology (RSM), based on a four-factor-three-level Box–Behnken design (BBD), the extraction parameters, namely, temperature, extraction time, solvent-to-material ratio, and extraction power, were optimized for maximizing the yields of HSYA and AHSYB. The maximum yield was obtained at a temperature of 66 °C with an extraction time of 36 min, solvent-to-material ratio of 16 mL/g, and the extraction power of 150 W, which was adjusted according to the actual conditions. The HSYA and AHSYB contents were determined using high performance liquid chromatography (HPLC). The yield and the comprehensive evaluation value of HSYA and AHSYB were calculated. The antioxidant activities of the extracts were determined using a ferric reducing antioxidant power (FRAP) kit and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging activity. The results suggested that the safflower extracts possessed obvious ferric reducing and DPPH radical scavenging activities. The antioxidant activity increased with increasing concentration. The results suggested that optimizing the conditions of ultrasonic extraction using RSM can significantly increase the yields of HSYA and AHSYB from safflower. The safflower extracts showed better antioxidant activity. This study can encourage future research on cardiovascular and cerebrovascular diseases.

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Section: Discussionmentioning

confidence: 99%

Simultaneous Optimization of the Ultrasonic Extraction Method and Determination of the Antioxidant Activities of Hydroxysafflor Yellow A and Anhydrosafflor Yellow B from Safflower Using a Response Surface Methodology

Zhang

Jin

et al. 2020

Molecules

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“…2008; Paprocki and Szczepanski 2011, 2013a, b; Urner et al. 2013; Lengler and Steger 2017; Voronenko and Lindner 2018). The problem of coding and decoding spike trains is central both to understanding how neurons process information (Rieke et al.…”

Section: Methodsmentioning

confidence: 99%

Information processing in the LGN: a comparison of neural codes and cell types

et al. 2019

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To understand how anatomy and physiology allow an organism to perform its function, it is important to know how information that is transmitted by spikes in the brain is received and encoded. A natural question is whether the spike rate alone encodes the information about a stimulus ( rate code ), or additional information is contained in the temporal pattern of the spikes ( temporal code ). Here we address this question using data from the cat Lateral Geniculate Nucleus (LGN), which is the visual portion of the thalamus, through which visual information from the retina is communicated to the visual cortex. We analyzed the responses of LGN neurons to spatially homogeneous spots of various sizes with temporally random luminance modulation. We compared the Firing Rate with the Shannon Information Transmission Rate , which quantifies the information contained in the temporal relationships between spikes. We found that the behavior of these two rates can differ quantitatively. This suggests that the energy used for spiking does not translate directly into the information to be transmitted. We also compared Firing Rates with Information Rates for X-ON and X-OFF cells. We found that, for X-ON cells the Firing Rate and Information Rate often behave in a completely different way, while for X-OFF cells these rates are much more highly correlated. Our results suggest that for X-ON cells a more efficient “temporal code” is employed, while for X-OFF cells a straightforward “rate code” is used, which is more reliable and is correlated with energy consumption.

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“…In the realm of image analysis, CV was employed by Singh & Singh (2019) to detect video frame and region duplication forgery, while in the context of robot path planning, it was harnessed for multiple objectives by Salmanpour et al (2017). Scientific research, such as the analysis of neuronal spike trains, also integrated CV, recommending a G1-CV approach for optimal developmental face ventilation mode selection, as demonstrated by Lengler & Steger (2017) and Z. Zhou et al (2018).…”

Section: Introductionmentioning

confidence: 99%

Variable Sample Size Control Charts for Monitoring the Multivariate Coefficient of Variation Based on Median Run Length and Expected Median Run Length

Karimi,

Khaw,

Chew

et al. 2023

JST

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The monitoring of a well-functioning process system has always held significant importance. In recent times, there has been notable attention towards employing control charts to oversee both univariate and multivariate coefficients of variation (MCV). This shift is in response to the concern of erroneous outcomes that can arise when traditional control charts are applied under the condition of dependent mean and standard deviation, as highlighted by prior research. To address this, the remedy lies in adopting the coefficient of variation. Furthermore, this study underscores the application of MCV in scenarios where multiple quality attributes are simultaneously under surveillance within an industrial process. This aspect has demonstrated considerable enhancement in chart performance, especially when incorporating the variable sample size (VSS) feature into the MCV chart. Adaptive VSS, evaluated through metrics like median run length (MRL) and expected median run length (EMRL), is also integrated for MCV monitoring. In contrast to earlier studies that predominantly focused on average run length (ARL), this research acknowledges the potential inaccuracies in ARL measurement. In this study, two optimal designs for VSS MCV charts are formulated by minimizing two criteria: firstly, MRL; and secondly, EMRL, both accounting for deterministic and unknown shift sizes. Additionally, to assess the distribution's variability in run lengths, the study provides the 5th and 95th percentiles. The research delves into two VSS schemes: one with a defined small sample size (nS), and another with a predetermined large sample size (nL) for the initial subgroup (n(1)). The approach taken involves the development of a Markov chain method for designing and deriving performance measures of the proposed chart. These measures include MRL and EMRL. Moreover, a comparative analysis between the proposed chart's performance and the standard MCV chart (STD) is presented in terms of MRL and EMRL criteria. The outcomes illustrate the superiority of the proposed chart over the STD MCV chart for all shift sizes, whether they are upward or downward, and when n(1) equals nS or nL.

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Note on the coefficient of variations of neuronal spike trains

Cited by 9 publications

References 23 publications

Simultaneous Optimization of the Ultrasonic Extraction Method and Determination of the Antioxidant Activities of Hydroxysafflor Yellow A and Anhydrosafflor Yellow B from Safflower Using a Response Surface Methodology

Simultaneous Optimization of the Ultrasonic Extraction Method and Determination of the Antioxidant Activities of Hydroxysafflor Yellow A and Anhydrosafflor Yellow B from Safflower Using a Response Surface Methodology

Information processing in the LGN: a comparison of neural codes and cell types

Variable Sample Size Control Charts for Monitoring the Multivariate Coefficient of Variation Based on Median Run Length and Expected Median Run Length

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