Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.

Calcium imaging has been used as a promising technique to monitor the dynamic activity of neuronal populations. However, the calcium trace is temporally smeared which restricts the extraction of quantities of interest such as spike trains of individual neurons. To address this issue, spike reconstruction algorithms have been introduced. One limitation of such reconstructions is that the underlying models are not informed about the biophysics of spike and burst generations. Such existing prior knowledge might be useful for constraining the possible solutions of spikes. Here we describe, in a novel Bayesian approach, how principled knowledge about neuronal dynamics can be employed to infer biophysical variables and parameters from fluorescence traces. By using both synthetic and in vitro recorded fluorescence traces, we demonstrate that the new approach is able to reconstruct different repetitive spiking and/or bursting patterns with accurate single spike resolution. Furthermore, we show that the high inference precision of the new approach is preserved even if the fluorescence trace is rather noisy or if the fluorescence transients show slow rise kinetics lasting several hundred milliseconds, and inhomogeneous rise and decay times. In addition, we discuss the use of the new approach for inferring parameter changes, e.g. due to a pharmacological intervention, as well as for inferring complex characteristics of immature neuronal circuits.

The thesis is focused on the recognition methods study used in one-dimensional signal processing. A lot of recognition methods exist, this thesis briefly describes the principle of some of them, e.g. artificial neural networks, fuzzy systems, expert systems and decision trees. Dynamic time warping (DTW) method has been chosen for signal processing available from UBMI database. DTW can be used as a non-linear signal processing method. The result of this method is to determine the similarity of two compared signals on the basis of their distance calculation. One of the reasons for choosing this method was the possibility of various length signal processing. The principle of the method as well as the calculation of the distance between two input data sequences is described in the thesis. DTW path finding method is also mentioned. The method was applied on randomly selected numbers and a set of simulated signals. The method was applied to ECG and action potential signals recorded on the isolated rabbit heart. DTW was used to evaluate shape changes of these signals in repeated phases of the experiment known as ischemia and reperfusion. Selected cardiac cycles were detected and included into different experiment phases on the basis of calculated distance results using DTW. Sensitivity was selected as an evaluative criterion of this classification method. It reached a value of 65%. DTW algorithm was further tested on the selected cardiac cycle mapping to the corresponding minute record in the selected experiment phase. It reached a sensitivity of 68.3%. The motion artifact appearance was monitored using DTW on AP signals. The method functioned more precisely on signals measured in ischemia phases. Along with the above mentioned, the thesis discusses all aspects of heart electrical manifestation activities called as ECG signals and action potentials, such as origin, propagation, recording, post-processing and measuring out.

This thesis deals with the problem with cluster analysis and biosignal classification options. The principle of cluster analysis, methods for calculating distances between objects and the standard process in the implementation of clustering are described in the first part. For biosignals processing,it is necessary to get familiar with the primary parameters of these signals in the following sections of thesis, process biosignals and methods for recording of action potentials described. Based on studying different clustering methods is presented a program with the applied method kmedoid in the next section of this thesis. The steps of this program are described in detail and in the end of thesis functionality is tested on a database of signals ÚBMI.