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Recent advances in microelectronics and microfabrication technology have allowed for developing novel high-density microelectrode arrays, incorporating thousands of densely-arrayed microelectrodes for measuring and stimulating neuronal activity. The high spatiotemporal resolution of high-density microelectrode array (HD-MEA) technology offers possibilities to conduct novel experiments, which are not possible by using traditional techniques. At the same time, the vast amount of data, recorded by hundreds to thousands of electrodes, also poses new challenges to performing signal processing and data analysis. This thesis presents the development and evaluation of spike sorting techniques, as well as experiments on targeted stimulation and mapping postsynaptic signals by combining the HD-MEA technology with the traditional patch clamp technique. For observed signals that constitute a linear mixture of a set of source signals, independent component analysis (ICA) can be used to blindly deconvolve the data and to extract the individual source signals. ICA offers great potential to alleviate the problem of spike sorting in HDMEA recordings, as it represents an unsupervised method to separate the neuronal sources. The characteristics of extracellular signals of retinal ganglion cells (RGCs), recorded at high spatiotemporal resolution by HD-MEAs, were analyzed. This analysis revealed that the recorded data cannot be modeled as a purely linear mixture. Artificial realistic HD-MEA recordings of RGC activity were simulated for different neuronal densities and used to evaluate the performance of ICA as a stand-alone tool for spike sorting. In order to overcome the limitations arising from the nonlinearity of the sources, an iterative algorithm combining PCA and clustering techniques with ICA was developed. The spike sorting performance of the unsupervised ICAbased algorithm was found to strongly depend on cell density and spike amplitudes. A system combining an upright microscope, the HD-MEA, and a conventional patch clamp setup was built. Image alignment software was developed to automatically detect the electrode identities on acquired images. Hardware and software components of the HD-MEA system were modified in order to route the signals of the patch clamp

Methods for synaptic connectivity mapping, neuronal stimulation, and spike sorting using high-density microelectrode arrays and patch clamp recordings