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bioRxiv : the preprint server for biology · 2023

Automated system for training and assessing string-pulling behaviors in rodents.

ПрепринтСтатьяНазвание и аннотация: Перевод пока не подготовлен
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Jordan Gianna A, Vishwanath Abhilasha, Holguin Gabriel, Bartlett Mitchell J, Tapia Andrew K, Winter Gabriel M, Sexauer Morgan R, Stopera Carolyn J, Falk Torsten, Cowen Stephen L

Аннотация

String-pulling tasks have been used for centuries to study coordinated bimanual motor behavior and problem solving. String pulling is rapidly learned, ethologically grounded, and has been applied to many species and disease conditions. Typically, training of string-pulling behaviors is achieved through manual shaping and baiting. Furthermore, behavioral assessment of reaching, grasping, and pulling is often performed through labor intensive manual video scoring. No system, to our knowledge, currently exists for the automated shaping and assessment of string-pulling behaviors. Here we describe the PANDA system (Pulling And Neural Data Analysis), an inexpensive hardware and software system that utilizes a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for assessment and training of string-pulling behaviors and synchronization with neural recording data. We demonstrate this system in unimplanted rats and rats implanted with electrodes in motor cortex and hippocampus and show how the PANDA system can be used to assess relationships between paw movements and single-unit and local-field activity. We also found that automating the shaping procedure significantly improved overall performance, with rats regularly pulling >100 meters during a 15-minute session. In conclusion, the PANDA system will be of general use to researchers investigating motor control, motivation, and motor disorders such as Parkinson's disease, Huntington's disease, and stroke. It will also support the investigation of neural mechanisms involved in sensorimotor integration.

ИП Урсолов Владлен Сергеевич

ИНН 500118482352 · ОГРНИП 314500117700049

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