Abstract
Mazes are a fundamental and widespread tool in behavior and systems neuroscience research in rodents, especially in spatial navigation and spatial memory investigations in freely behaving animals. However, their form and inflexibility often restrict potential experimental paradigms that involve multiple or adaptive maze designs. Unique layouts often lead to elevated costs, whether financially or in terms of time investment from scientists. To alleviate these issues, we have developed an automated, modular maze system that is flexible and scalable. This open source Adapt-A-Maze (AAM) system will allow for experiments with multiple track configurations in rapid succession. Additionally, the flexibility can expedite prototyping of behavioral paradigms. Automation ensures less variability in experimental parameters and higher throughput. Finally, the standardized componentry enhances experimental repeatability within labs and replicability across labs. Our maze was successfully used across labs, in multiple experimental designs, with and without extracellular or optical recordings, in rats. The AAM system presents multiple advantages over current maze options and can facilitate novel behavior and systems neuroscience research.
Significance statement We have developed an open source, modular maze system (the Adapt-A-Maze, AAM) that enables any lab interested in rodent behavior and cognition to create standard and unique mazes for their research. The AAM uses modular track pieces that can be combined to create a wide array of maze types. The AAM system also included reward wells with lick detection and pneumatic barriers. All aspects of the maze can be controlled via TTL signals to automate tasks and improve repeatability of experiments. The AAM system is expected to help labs quickly and inexpensively set up recordable experiments to advance our understanding of the neurophysiology underlying behavior and cognition.
Footnotes
We thank the Brandeis NSF I-Corps Program for guidance during product development, our I-Corps teammates Xin Yao Lin and Faye Raymond for helpful discussions during development, Francisco Mello for his assistance in fabrication of maze components, Andrew Alvarenga (Grasshopper Machine Werks, LLC.) for technical assistance on the pneumatic barriers. We thank many of our colleagues who helped to develop the maze, especially the barriers, including Dwayne "Whitey" Adams, Shahaf Weiss, Emily Aery Jones, Ryan Young, Caine Rees, Noah Moore, Jianing Yu, and Kevin Allen. We would also like to thank all members of the Jadhav and van der Meer labs for helpful comments and feedback during the design and implementation of the maze system.
Authors report no conflict of interest
This work was supported by a Smith Foundation Odyssey Award and NIH grants R01MH112661, R01MH120228 and R01DC020640 to S.P.J., and a Brandeis Innovation SPROUT grant. B.S.P was supported by the NIH NINDS T32 (NS 0072-92). J.M.O and J.H.B were supported by NIH NINDS T32 (NS 7292-33). J.M.O. was also supported by the Swartz Foundation and NIH NIMH K99 (1K99MH128579). M.vdM. was supported by NSF CAREER IOS-1844935.
↵*These authors contributed equally.
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