RT Journal Article
SR Electronic
T1 3D-Printed Capacitive Sensor Objects for Object Recognition Assays
JF eneuro
JO eNeuro
FD Society for Neuroscience
SP ENEURO.0310-20.2020
DO 10.1523/ENEURO.0310-20.2020
VO 8
IS 1
A1 Kasey P. Spry
A1 Sydney A. Fry
A1 Jemma M.S. DeFillip
A1 S. Griffin Drye
A1 Korey D. Stevanovic
A1 James Hunnicutt
A1 Briana J. Bernstein
A1 Eric E. Thompson
A1 Jesse D. Cushman
YR 2021
UL http://www.eneuro.org/content/8/1/ENEURO.0310-20.2020.abstract
AB Object recognition tasks are widely used assays for studying learning and memory in rodents. Object recognition typically involves familiarizing mice with a set of objects and then presenting a novel object or displacing an object to a novel location or context. Learning and memory are inferred by a relative increase in time investigating the novel/displaced object. These tasks are in widespread use, but there are many inconsistencies in the way they are conducted across labs. Two major contributors to this are the lack of consistency in the method of measuring object investigation and the lack of standardization of the objects that are used. Current video-based automated algorithms can often be unreliable whereas manual scoring of object investigation is time consuming, tedious, and more subjective. To resolve these issues, we sought to design and implement 3D-printed objects that can be standardized across labs and use capacitive sensing to measure object investigation. Using a 3D printer, conductive filament, and low-cost off-the-shelf components, we demonstrate that employing 3D-printed capacitive touch objects is a reliable and precise way to perform object recognition tasks. Ultimately, this approach will lead to increased standardization and consistency across labs, which will greatly improve basic and translational research into learning and memory mechanisms.