Core 3, Behavior Automation Working memory, the ability to temporarily hold multiple pieces of information in mind for manipulation, is central to virtually all cognitive abilities. This multi-component research project aims to comprehensively dissect the neural circuit mechanisms of this ability across multiple brain areas. The individual parts of the project cohere conceptually, in part, because they all involve rodents trained to perform a type of decision-making task that is based on the gradual accumulation of sensory evidence and thus relies on working memory. To produce enough subjects for these experiments, this Core will scale up an existing high-throughput rat training facility run by technicians and adapt it for mice. This expansion will quintuple the project?s capacity for rodent training. To do so, we will take advantage of the expertise of the project leader in developing and managing such a training facility for sophisticated cognitive tasks in an existing virtual reality infrastructure, software, and hardware. Once this facility is operational, the Core will manage it and troubleshoot problems as needed. It will develop new hardware and software components for training rigs to make technician interventions as reproducible and error-free as possible. Because the most crucial and time-consuming aspect of mouse virtual training is ensuring that the head-fixed animal is properly aligned to the ball and the projection system, the Core will develop an automated alignment system based on image registration and actuators to replace the current manual alignment. It will develop software tools and standardized technician procedures to ensure consistency in rodent training, prevent training errors, detect hardware failures, and monitor the health of the animals. This centralized facility will promote rigor and reproducibility by reducing variability in animal training across labs, increase the rate of data acquisition, and free personnel to focus on designing and carrying out creative experiments. In the long run, the entire neuroscience community will benefit from this effort, as the software and hardware tools and management protocols produced will be made freely available, along with their documentation. These tools will enable other researchers to introduce automated training for well-controlled cognitive tasks in their own laboratories, leading to improved efficiency, rigor, and reproducibility in behavioral research across the field of neuroscience.
| Scott, Benjamin B; Thiberge, Stephan Y; Guo, Caiying et al. (2018) Imaging Cortical Dynamics in GCaMP Transgenic Rats with a Head-Mounted Widefield Macroscope. Neuron 100:1045-1058.e5 |
| Pinto, Lucas; Koay, Sue A; Engelhard, Ben et al. (2018) An Accumulation-of-Evidence Task Using Visual Pulses for Mice Navigating in Virtual Reality. Front Behav Neurosci 12:36 |
| Deverett, Ben; Koay, Sue Ann; Oostland, Marlies et al. (2018) Cerebellar involvement in an evidence-accumulation decision-making task. Elife 7: |
