Over the last two decades, technological advances in the fields of microchip and electronics manufacturing have enabled an increase in the production and use of silicon-based multi-electrode arrays. (Singer, 2000; Morin et al., 2005) These multi-electrode arrays or MEA for short, have come in a variety of shapes and materials, but fall into two broad classes: thin and sharp (implantable) or dishbased (planar). Although many investigations are currently undertaking research in vivo with implantable versions, this chapter focuses on applications of planar MEAs (pMEA), which are very well suited for in vitro experiments with slice or dissociated cells preparations. This chapter illustrates the utility and advantages of pMEAsin electrophysiological investigations with acute hippocampal slices, while introducing a new generation of conformally designed higher-density pMEAs as an adjuvant approach to facilitate and enhance MEA-based research. Currently, the research being undertaken on pMEAs ranges from studying processes of neuronal plasticity underlying learning and memory, to tracking activity development in networks, and also pharmacological drug screening and testing. These diverse applications can be classified, based on the intricacy of their methodology, into the following nonmutually exclusive categories: (1) MEAs can be used as a multitude of single independent electrodes for rapid high-throughput experiments; (2) the spatial relations between electrode tips can be used synergistically to map electrical activity to tissue location; (3) recording simultaneously from multiple electrodes allows correlation of temporal information, which is not possible with many recordings from single electrodes; (4) the combination of spatial and temporal monitoring reveals the spatiotemporal dynamics of the neuronal network; (5) the ability to maintain cultured preparations on pMEAs allows longterm physiological investigations; and (6) recording and stimulating through the pMEA creates two-way communication with the tissue that is indispensable for investigating and developing neuroprosthetic applications. High-throughput applications involve sampling several electrodes out of the total number on the MEA and selecting a representative one, or treating subgroups statistically as multiple samples from a homogeneous population. Electrodes within a particular cytoarchitectural region of a slice usually record similar neural responses. This redundancy of observed signals can be used to enhance the statistical significance of results by grouping responses into larger sample sizes. Similar time savings are achieved in cell cultures, where the multitude of electrodes records the activity of numerous cells at the same time, thereby decreasing the number of individual experiments needed to reach a significant population sample. Such high-throughput use of MEAs as biosensors has been applied to drug screening using cell culture (Pine, 1980; Gross et al., 1995) and hippocampal slice rhythmic activity (Shimono et al., 2000). In the first case, drugs are classified according to changes in the firing activity of neuronal cells cultured on MEAs (Gross et al., 1995, 1999). In the second case, changes in the frequency of carbachol-induced theta rhythmic oscillations in hippocampal slices are correlated with specific drug properties (Shimono et al., 2000). In both cases, the MEAs provided multiple sample points in different regions of the network, which enabled either a quick selection of an optimal site or averaging several channels for greater statistical accuracy. In contrast to using array electrodes as individual and independent streams of data, the spatial arrangement of electrodes can be used to generate spatial maps of the activity in a slice. Any parameter of the recorded potentials can be plotted in a color-coded matrix according to the relative spatial positions of the electrodes in order to generate topographic activity maps. Such spatial activity maps can be matched to a picture of the slice showing the actual electrode positions in order to visualize the activity in relation to the subregions of a slice (Shimono et al., 2000) or map the spatial extent of a response along a network (Jimbo and Robinson, 2000). In addition, if electrodes are close enough to each other, they enable current source density (CSD) analysis, which can elucidate the origins and meaning of the complex field potentials recorded (Wheeler and Novak, 1986). The ability to simultaneously record from all the MEA electrodes over time enables correlation of activity between different parts of a network in order to study their patterns and plasticity in cell and tissue preparations. The temporal sequence of firing of ensembles of cells can provide information on network states. Beggs and Plenz (2003) analyzed cell bursting avalanches to describe the stability of the network. Jimbo et al. (1999) reported on time-dependent synaptic plasticity in networks of cultured cells in observing that connections between cells that fired within 20 msec before the other were potentiated after tetanus, whereas connections between cells negatively correlated within 20 msec were depressed. pMEAs combine spatial and temporal information and enable the conversion of static spatial activity maps into dynamic spatiotemporal map sequences. These series of maps can be joined as frames of amovie to visually trace the propagation of spontaneous, evoked, or rhythmic activity across the slice. For example, Novak and Wheeler (1989) studied the temporal propagation of seizure activity, and Shimono et al. (2000) localized and spatiotemporally followed the origin of theta rhythm generated by carbachol in a slice, both using CSD analysis of the signals recorded from the MEA. The surface of pMEAs is ideal for long-term tissue cultures of both slices and dissociated cells, as it provides a flat, biocompatible, and sterilizable support with embedded electrodes that can continuously monitor culture activity without disrupting the closed system. Longer-term experiments can track changes in activity and plasticity of developing cultures and networks (Gross and Schwalm, 1994; Stoppini et al., 1997; Thiebaud et al., 1997; Jahnsen et al., 1999) under different chronic pharmacological treatments (Shimono et al., 2002). Although several neuroprosthetics, such as cochlear, cortical (Chapin et al., 1999), and retinal (Humayun et al., 2003) devices rely on implantable in vivoMEA technology, pMEAs still play a major role in understanding network connectivity and dynamics (Meister et al., 1994; Warland et al., 1997). pMEAs are being used as an in vitro testing platform to first characterize the information processing of the target neuronal network, before undertaking in vivo experiments. In our current goal to replace the CA3 hippocampal area with a microchip (FPGA/VLSI) implementation of a nonlinear model of CA3 (Berger et al., 2001), we are using pMEAs to provide a functional proof-of-principle.pMEAexperiments allowus first to generate nonlinear models, then to test hardware implementations in order to change parameters and conditions rapidly, cost effectively, and with fewer animals. Similarly, the retinal prosthesis project relies on understanding underlying network dynamics and plasticity of the retina in order to transform incident light into an electrical stimulation pattern that will produce correct visual percepts (Humayun and Weiland, personal communications). Retinal stimulation and recording experiments are thus currently being undertaken on pMEAs to develop a nonlinear mathematical model of the retinal network that will be implemented in the next generation of retinal prostheses (Chichilnisky and Kalmar, 2003; Frechette et al., 2005). © 2006 Springer Science+Business Media, Inc. All rights reserved.