Yttri, E. A. | Search bioRxiv

bioRxiv2019

The CMU Array. A 3D Nano-Printed, Fully Customizable Ultra-High-Density Microelectrode Array

Microelectrode arrays (MEAs) provide the means to record electrophysiological activity fundamental to both basic and clinical neuroscience (e.g. brain-computer interfaces). Despite recent advances, current MEAs have significant limitations - including low recording density, fragility, expense, and the inability to optimize the probe to individualized study or patient needs. Here we address the technological limitations through the utilization of the newest developments in 3D nanoparticle printing.1 Our CMU Arrays possess previously impossible electrode densities (> 6000 channels/cm2) with tip diameters as small as 10 m. Most importantly, the probes are entirely customizable owing to the adaptive manufacturing process. Any combination of individual shank lengths, impedances, and layouts are possible. This is achieved in part via our new multi-layer, multi material, custom 3D-printed circuit boards, a fabrication advancement in itself. This device design enables new experimental avenues of targeted, large-scale recording of electrical signals from a variety of biological tissues.

neuroscience↗

bioRxiv2019

Build a better mouse task: can an open-source rodent joystick enhance reaching behavior outcomes through improved monitoring of real-time spatiotemporal kinematics?

For decades, advanced behavioral tasks have only been used in human and non-human primates. However, with improved analytical and genetic techniques, there has been a growing drive to implement complex reaching, decision-making, and reaction time tasks - not in primates - but in rodents. Here, we assess the hypothesis that a mouse can learn a cued reaction time task. Moreover, we tested multiple training regimens and found that introducing elements of the reaction time task serially hindered, rather than helped task acquisition. Additionally, we include a step-by-step manual for inexpensive implementation and use of a rodent joystick for behavioral analysis. Task and analysis code for the evaluated behaviors are included such that they may be replicated and tested further. With these, we also include code for a probabilistic reward two-arm bandit task. These various tasks, and the method to construct and implement them, will enable greatly improved study of the neural correlates of behavior in the powerful mouse model organism. In summary, we have tested and demonstrated that mice can learn sophisticated tasks with A joystick, and that targeted task design provides a significant advantage. These results of this study stand to inform the implementation of other sophisticated tasks using the mouse model.

animal behavior and cognition↗