Vilazodone is a partial 5-HT1A receptor agonist and a selective serotonin reuptake inhibitor (SSRI). Acute administration caused a dose-dependent decrease in dorsal raphe (DR) serotonin (5-HT) neuron firing rates. Vilazodone significantly decreased DR 5-HT neuronal firing following 2-day administration, which was shown to recover completely after 14-day administration. The 2-day administration of vilazodone significantly decreased firing in ventral tegmental area dopamine neurons; this effect persisted after 14-day treatment. The firing rate of norepinephrine neurons in the locus coeruleus was not significantly altered following 2-day treatment but did decrease following 14-day treatment. In the hippocampus, 14-day treatment with vilazodone significantly enhanced tonic activation, while having no effect on 5-HT reuptake. Vilazodone produced effects similar to conventional SSRIs while also inducing alterations in monoaminergic neurons that may be associated with its 5-HT1A properties and may have a role in the field of treatment resistant depression.

The signal collected by a macroelectrode inserted in the cat's optic tract is made up of a slow component and fast variations (spike discharge). The mean amplitude of the peaks of the fast variations appears to be correlated with the slow component. The spike discharge is studied by an amplitude discriminator; the effect of filtering and overlapping are discussed, and it is seen that the mean counting value (weighted mean) obtained from the amplitude discriminator is a representation of the number of firings occurring in the neighbourhood of the macroelectrode. The curves obtained for the weighted mean show a high degree of similarity with the slow component. The relationship between the slow component and the weighted mean is calculated in the form of a transfer function. This transfer function appears to be the same for a wide range of stimulus conditions. These findings suggest that dendritic activity has little to do with generation of optic tract potentials. It also indicates that the nerve firing must be associated in the optic tract with a prompt positive overshoot allowing building up of positive macropotentials. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

微電列陣今已被廣泛用於各種生理和理的研究。通過把微電列陣連接到肌肉細胞，細胞外的電生理信號會被有效地記錄，我們進而對尖峰信號的傳播模式進行分析，以便了解肌肉收縮的模式。本文旨在對觀測到的電生理信號進行統計模型擬合，從而獲得對於肌肉收縮模式的統計推論。我們提出了三種方法用以提取尖峰信號的激活時間，分別為均值方差法、局部加權回歸法(LOWESS方法)和Butterworth濾波法。然後對抽取出來的尖峰信號應用隨機Hough轉換，識別出多個傳播的信號波，從而得到肌肉收縮的率。對於每個信號波，我們建立了兩個模型來描述信號的傳播模式，即圓形波陣面模型和線性波陣模型。通過這兩種模型擬合，表達信傳播特徵的參數可被估算，例如激發信號波的起源位和起始時間，信號的傳播方向以及速度等。利用根據兩種模型合成的模擬數據，我們證明了隨機霍夫轉換算法和模型擬合的有效性及準確性，並把文中提出的算法用於大鼠心肌培養細胞的一個數據集。由此數據集得出的結果可以用於監測細胞的電生理變化，從而闡明藥物或環條件對心肌細胞產生的影響。 / The microelectrode array (MEA) has been widely used in physiological and pharmacological research. By attaching the MEA system to muscle cells, extracellular electrophysiological signals can be recorded, and the spike-signal propagation pattern can be analyzed for understanding the muscle contraction pattern. This thesis aims at providing a statistical framework for analyzing the muscle contraction pattern from the observed electrophysiological signals. We first provides three methods for extracting the activation time of signal spikes: the mean-variance method, the LOWESS smoothing method, and the Butterworth filtering method. The randomized Hough transform is then applied to the signal spikes to identify the multiple propagating waves, which gives the rate of beating. For each propagating wave, we propose two models to describe the signal propagation pattern, namely the circular wavefront model and the linear wavefront model. By fitting these two models, parameters that characterize the signal propagation can be estimated, such as the origin and time of excitation, the direction of propagation, and the speed of propagation. We demonstrate the performances of the randomized Hough tranform algorithm and model fitting in two simulation studies, and apply these approaches to a real data set of cultured cardiac myocytes of rats. The result may be used to monitor the electrophysiological changes and thereby elucidate the drug effect or environmental condition on cardiomyocytes. / Detailed summary in vernacular field only. / Lu, Jiayi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 62-65). / Abstracts also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivating problem --- p.1 / Chapter 1.2 --- An overview of MEA --- p.2 / Chapter 1.3 --- Electrophysiology of cardiac myocytes --- p.5 / Chapter 1.4 --- Organization --- p.5 / Chapter 2 --- A generative model for MEA data --- p.7 / Chapter 2.1 --- Circular wavefront model --- p.9 / Chapter 2.2 --- Linear wavefront model --- p.11 / Chapter 3 --- Computing method for MEA signals --- p.13 / Chapter 3.1 --- Preliminaries --- p.13 / Chapter 3.1.1 --- Locally weighted scatterplot smoothing(LOWESS) --- p.13 / Chapter 3.1.2 --- Butterworth filter --- p.16 / Chapter 3.1.3 --- Hough transform --- p.16 / Chapter 3.1.4 --- Nonlinear minimization --- p.21 / Chapter 3.2 --- Overall procedure for MEA data analysis --- p.24 / Chapter 3.3 --- Extract the spike activation time --- p.25 / Chapter 3.4 --- Identification of multiple propagating waves --- p.28 / Chapter 3.5 --- Model fitting --- p.29 / Chapter 3.5.1 --- Circular wavefront model --- p.29 / Chapter 3.5.2 --- Linear wavefront model --- p.33 / Chapter 4 --- Simulation study based on synthesized data --- p.35 / Chapter 4.1 --- Wave detection using Hough transform --- p.35 / Chapter 4.1.1 --- Data synthesis from linear wavefront model --- p.35 / Chapter 4.1.2 --- Performance of the randomized Hough transform --- p.38 / Chapter 4.2 --- Model fitting for signal propagating pattern --- p.38 / Chapter 4.2.1 --- Data Synthesis from circular wavefront model --- p.38 / Chapter 4.2.2 --- Performance of the model fitting algorithm --- p.42 / Chapter 5 --- Real data application --- p.47 / Chapter 5.1 --- Data set --- p.47 / Chapter 5.2 --- Extract the spike activation time --- p.49 / Chapter 5.3 --- Identify multiple propagating waves --- p.52 / Chapter 5.4 --- Model fitting --- p.52 / Chapter 5.4.1 --- Fitting the circular wavefront model --- p.52 / Chapter 5.4.2 --- Fitting the linear wavefront model --- p.55 / Chapter 5.4.3 --- Comparison of the two models --- p.56 / Chapter 6 --- Conclusions and future directions --- p.60 / Chapter 6.1 --- Conclusions --- p.60 / Chapter 6.2 --- Future directions --- p.61

No description available.

Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on July 17, 2009. Major professor: Dr. Robert Szlavik. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degrees [of] Master of Science in Engineering, with specialization in Biomedical Engineering, Bachelor's of Science in Electrical Engineering." "June 2009." Includes bibliographical references (p. 22).

Thesis (doctoral)--Bayerische Ludwig-Maximilians-Universität, Munich, 1934.

Thesis (doctoral)--Bayerische Ludwig-Maximilians-Universität, Munich, 1934.

Thesis (M.S.)--University of Texas at Dallas, 2008. / Includes vita. Includes bibliographical references (leaves 77-78)

Evoked potentials are electrical signals produced by the nervous system in response to a stimulus. In general these signals are noisy with a low signal to noise ratio. The aim was to investigate ways of extracting the evoked response within an evoked potential recording, achieving a similar signal to noise ratio as conventional averaging but with less repetitions per average. In this thesis, evolutionary algorithms were used in three ways to extract the evoked potentials from a noisy background. First, evolutionary algorithms selected the cut—off frequencies for a set of filters. A different filter or filter bank was produced for each data set. The noisy signal was passed through each filter in a bank of filters the filter bank output was a weighted sum of the individual filter outputs. The goal was to use three filters ideally one for each of the three regions (early, middle and late components), but the use of five filters was also investigated. Each signal was split into two time domains: the first 3Oms of the signal and the region 30 to 400ms. Filter banks were then developed Ibr these regions separately. Secondly, instead of using a single set of filters applied to the whole signal, different filters (or combinations of filters) were applied at different times. Evolutionary algorithms are used to select the duration of each filter, as well as the frequency parameters and weightings of the filters. Three filtering approaches were investigated. Finally, wavelets in conjunction with an evolutionary algorithm were used to select particular wavelets and wavelet parameters. A comparison of these methods with optimal filtering methods and averaging was made. Averages of 1 0 signals were ibund suitable, and time-varying techniques were Ibund to perlbrm better than applying one filter to the whole signal

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