Learning Cultured Neuronal Network Evolution Using False Discovery Rate Analysis

Despite numerous studies carried out using Multi Electrode Arrays (MEAs), there are no quantitative studies that assess how the development of dissociated rat cortical neurons can be affected by chronic external stimulation. Furthermore, there is a lack of quantitative analysis tools in use for processing spike data sets recorded from large neuronal populations. With this work, we want to emphasize the importance of using statistical analysis as a mathematical tool to identify functional and significant electrical connections, to quantify the temporal evolution and the early development of dissociated cortical neurons when presented with external stimulation. The False Discovery Rate technique we propose guarantees that when analyzing the neuronal evoked responses, we are accounting for the natural variability and randomness that are typical characteristics of the nervous system. Our preliminary findings suggest that electrical stimulation has significant effects on neuron electrical activity.