Development of extracellular electrophysiology methods for scalable neural recording

Abstract

In order to map the dynamics of neural circuits in mammalian brains, there is a need for tools that can record activity over large volumes of tissue and correctly attribute the recorded signals to the individual neurons that generated them. High-resolution neural activity maps will be critical for the discovery of new principles of neural coding and neural computation, and to test computational models of neural circuits. Extracellular electrophysiology is a neural recording method that has been developed to record from large populations of neurons, but well-known problems with signal attribution pose an existential threat to the viability of further system scaling, as analyses of network function become more sensitive to errors in attribution. A key insight is that blind-source separation algorithms such as Independent Component Analysis may ameliorate problems with signal attribution. These algorithms require recording signals at much finer spatial resolutions than existing probes have accomplished, which places demands on recording system bandwidth. We present several advances to technologies in neural recording systems, and a complete neural recording system designed to investigate the challenges of scaling electrophysiology to whole brain recording. We have developed close-packed microelectrode arrays with the highest density of recording sites yet achieved, for which we built our own data acquisition hardware, developed with a computational architecture specifically designed to scale to over several orders of magnitude. We also present results from validation experiments using colocalized patch clamp recording to obtain ground-truth activity data. This dataset provides immediate insight into the nature of electrophysiological signals and the interpretation of data collected from any electrophysiology recording system. This data is also essential in order to optimize probe development and data analysis algorithms which will one day enable whole-brain activity mapping.