To fully exploit the recording capabilities provided by current and future generations of multi-electrode arrays, some means to eliminate the residual charge and subsequent artifacts generated by stimulation protocols is required. Custom electronics can be used to achieve such goals, and by making them scalable, a large number of electrodes can be accessed in an experiment. In this work, we present a system built around a custom 16-channel IC that can stimulate and record, within 3 ms of the stimulus, on the stimulating channel, and within 500 mus on adjacent channels. This effectiveness is achieved by directly discharging the electrode through a novel feedback scheme, and by shaping such feedback to optimize electrode behavior. We characterize the different features of the system that makes such performance possible and present biological data that show the system in operation. To enable this characterization, we present a framework for measuring, classifying, and understanding the multiple sources of stimulus artifacts. This framework facilitates comparisons between artifact elimination methodologies and enables future artifact studies.

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Multielectrode arrays (MEAs) have emerged as a leading technology for extracellular, electrophysiological investigations of neuronal networks. The study of biological neural networks is a difficult task that is further confounded by mismatches in electrode impedance. Electrode impedance plays an important role in shaping incoming signals, determining thermal noise, and influencing the efficacy of stimulation. Our approach to optimally reduce thermal noise and improving the reliability of stimulation is twofold minimize the impedance and match it across all electrodes. To this aim, we have fabricated a device that allows for the automated, impedance-controlled electroplating of micro-electrodes. This device is capable of rapidly (minutes) producing uniformly low impedances across all electrodes in an MEA. The need for uniformly low impedances is important for controlled studies of neuronal networks; this need will increase in the future as MEA technology scales from tens of electrodes to thousands.

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