TY - JOUR A1 - Kupnik, Mario A1 - Thielemann, Christiane T1 - In vitro platform for acoustic and electrophysiological investigations of ultrasound stimulation JF - Brain Stimulation N2 - Since the first time discovered that ultrasound can influence neuronal activity - more than half a century ago - a lot of progress has been made in this field. The possibilities of ultrasound for neuromodulation have been demonstrated in many experiments such as in vivo stimulation of rodent brain or of human cochlear, Further, in vitro experiments with hippocampal slices and other cell types have been performed as well. KW - Neuronales Netz KW - Ultraschall Y1 - 2017 U6 - https://doi.org/10.1016/j.brs.2017.01.465 VL - 10 IS - 2 SP - 501 ER - TY - CHAP A1 - Ciba, Manuel A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Application of spike train synchrony measure Spike‑contrast to quantify the effect of bicuculline on cortical networks grown on microelectrode arrays T2 - BMC Neuroscience KW - Mikroelektrode KW - Array Y1 - 2017 U6 - https://doi.org/10.1186/s12868-017-0372-1 VL - 2017 IS - 18 ER - TY - JOUR A1 - Bestel, Robert A1 - Appali, Revathi A1 - van Rienen, Ursula A1 - Thielemann, Christiane T1 - Effect of Morphologic Features of Neurons on the Extracellular Electric Potential: A Simulation Study Using Cable Theory and Electro-Quasi-Static Equations JF - Neural Computation N2 - Microelectrode arrays serve as an indispensable tool in electro-physiological research to study the electrical activity of neural cells, enabling measurements of single cell as well as network communication analysis. Recent experimental studies have reported that the neuronal geometry has an influence on electrical signaling and extracellular recordings. However, the corresponding mechanisms are not yet fully understood and require further investigation. Allowing systematic parameter studies, computational modeling provides the opportunity to examine the underlying effects that influence extracellular potentials. In this letter, we present an in silico single cell model to analyze the effect of geometrical variability on the extracellular electric potentials. We describe finite element models of a single neuron with varying geometric complexity in three-dimensional space. The electric potential generation of the neuron is modeled using Hodgkin-Huxley equations. The signal propagation is described with electro-quasi-static equations, and results are compared with corresponding cable equation descriptions. Our results show that both the geometric dimensions and the distribution of ion channels of a neuron are critical factors that significantly influence both the amplitude and shape of extracellular potentials. KW - Mikroelektrode KW - Array Y1 - 2017 U6 - https://doi.org/10.1162/neco_a_01019 VL - 2017 IS - 29 SP - 2955 EP - 2978 ER - TY - JOUR A1 - Ciba, Manuel A1 - Isomura, Takuya A1 - Jimbo, Yasuhiko A1 - Bahmer, Andreas A1 - Thielemann, Christiane T1 - Spike-contrast: A novel time scale independent and multivariate measure of spike train synchrony JF - Journal of Neuroscience Methods N2 - Background: Synchrony within neuronal networks is thought to be a fundamental feature of neuronal networks. In order to quantify synchrony between spike trains, various synchrony measures were developed. Most of them are time scale dependent and thus require the setting of an appropriate time scale. Recently, alternative methods have been developed, such as the time scale independent SPIKE-distance by Kreuz et al. New method: In this study, a novel time-scale independent spike train synchrony measure called Spike-contrast is proposed. The algorithm is based on the temporal “contrast” (activity vs. non-activity in certain temporal bins) and not only provides a single synchrony value, but also a synchrony curve as a function of the bin size. Results: For most test data sets synchrony values obtained with Spike-contrast are highly correlated with those of the SPIKE-distance (Spearman correlation value of 0.99). Correlation was lower for data containing multiple time scales (Spearman correlation value of 0.89). When analyzing large sets of data, Spike-contrast performed faster. Comparison of existing method: Spike-contrast is compared to the SPIKE-distance algorithm. The test data consisted of artificial spike trains with various levels of synchrony, including Poisson spike trains and bursts, spike trains from simulated neuronal Izhikevich networks, and bursts made of smaller bursts (sub-bursts). Conclusions: The high correlation of Spike-contrast with the established SPIKE-distance for most test data, suggests the suitability of the proposed measure. Both measures are complementary as SPIKE-distance provides a synchrony profile over time, whereas Spike-contrast provides a synchrony curve over bin size. KW - Synchrony KW - Neuronal Networks KW - Point processes KW - Time series analysis KW - Parallel spike trains KW - Neuronales Netz Y1 - 2017 U6 - https://doi.org/10.1016/j.jneumeth.2017.09.008 VL - 2018 IS - 293 SP - 136 EP - 143 ER - TY - JOUR A1 - Emmerich, Florian A1 - Thielemann, Christiane T1 - Optimizing dimensions of unipolar Teflon-FEP piezoelectrets with micro-system-technology JF - Journal of Physics: Conference Series N2 - In this work we present a new micro-system-technology based production process for unipolar ferroelectrets. Theoretical analysis of the influence of the air-gap size on the electric field distribution as well as on the induced charge on the electrodes shows superior performance of unipolar piezoelectrets with small air-gaps. For the production of these small air-gaps we developed a new design using a photoresist thermoforming master, an integrated micro-heater and shadow masks for metallization. Unipolar piezoelectrets produced with this technology exhibit increased d33-coefficients compared to designs in previous publications. These piezoelectrets are highly preferable for energy-harvesting applications, as they promise high electric power output. KW - Mikrosystemtechnik Y1 - 2017 VL - 2018 IS - 1052 SP - 1 EP - 4 ER - TY - JOUR A1 - Lieb, Florian A1 - Stark, Hans-Georg A1 - Thielemann, Christiane T1 - A stationary wavelet transform and a time-frequency based spike detection algorithm for extracellular recorded data JF - Journal of Neural Engineering N2 - Objective. Spike detection from extracellular recordings is a crucial preprocessing step when analyzing neuronal activity. The decision whether a specific part of the signal is a spike or not is important for any kind of other subsequent preprocessing steps, like spike sorting or burst detection in order to reduce the classification of erroneously identified spikes. Many spike detection algorithms have already been suggested, all working reasonably well whenever the signal-to-noise ratio is large enough. When the noise level is high, however, these algorithms have a poor performance. Approach. In this paper we present two new spike detection algorithms. The first is based on a stationary wavelet energy operator and the second is based on the time-frequency representation of spikes. Both algorithms are more reliable than all of the most commonly used methods. Main results. The performance of the algorithms is confirmed by using simulated data, resembling original data recorded from cortical neurons with multielectrode arrays. In order to demonstrate that the performance of the algorithms is not restricted to only one specific set of data, we also verify the performance using a simulated publicly available data set. We show that both proposed algorithms have the best performance under all tested methods, regardless of the signal-to-noise ratio in both data sets. Significance. This contribution will redound to the benefit of electrophysiological investigations of human cells. Especially the spatial and temporal analysis of neural network communications is improved by using the proposed spike detection algorithms. KW - spike detection KW - wavelet TEO KW - extracellular recording KW - Neuronales Netz Y1 - 2017 U6 - https://doi.org/10.1088/1741-2552/aa654b VL - 2017 IS - 14 SP - 1 EP - 13 ER -