Software Design And Implementation For Cultured Neuronal Network Signal Processing

Multi-Electrode Array (MEA) can non-invasively record the electrical activities of neuronal networks from multi-channels in real time for a long time, and it has been widely applied in neural experiment. It becomes an important topic of neuronal network dynamics research to develop corresponding software, which can provide reliable analysis of the data recorded by MEA.Nex (Neuroexplorer) is a commercial software used in neural experiment. It provides a series of common statistical algorithms for neuronal signal processing. Nex can meet most of the demand of neuronal network signal processing. But It does not support the extension, new algorithms can not be added.In this thesis, according to the need of neuronal network signal processing, the function modules of data converting program, algoritm program and image display program are designed. The data collected by MEA should be preprocessed (from binary file to text file), then processed by algoritm program,and the result is shown in picture.A software package for cultured neuronal network in Java implementeds on the basis of analysis and design on the module. The software package is integrated in BioLAB platform based on workflow developed by our lab, it integrates 20 statistical algorithms, including some common algorithms that Nex has, such as ISI histograms, Poincare map, Crosscorrelograms and so on. New algorithms (amplitudes and burst detection method that are improved on self-adaptive algorithm) and new functions (strong ability of graphic attributes editing) are added.In this paper, take the improved burst detection algorithm as an example, to process the data, and demonstrate the entire processing. The results are compared with the adaptive algorithms. It shows that the improved algorithm improve the accuracy of burst detection significantly.