ArticleJournal of neuroscience methods2024
Automated system for training and assessing reaching and grasping behaviors in rodents.
Article in Journal of neuroscience methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Decoupling of motor cortex to movement in Parkinson's dyskinesia rescued by sub-anaesthetic ketamine.Brain : a journal of neurology · 2025Article
- Computer vision for primate behavior analysis in the wild.Nature methods · 2025Review
- Effective training procedure for a simultaneous bimanual movement task in head-fixed mice.Frontiers in neural circuits · 2025Article
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
backgroundReaching, grasping, and pulling behaviors are studied across species to investigate motor control and problem solving. String pulling is a distinct reaching and grasping behavior that is rapidly learned, requires bimanual coordination, is ethologically grounded, and has been applied across species and disease conditions. NEW
methodHere we describe the PANDA system (Pulling And Neural Data Analysis), a hardware and software system that integrates a continuous string loop connected to a rotary encoder, feeder, microcontroller, high-speed camera, and analysis software for the assessment and training of reaching, grasping, and pulling behaviors and synchronization with neural data.
resultsWe demonstrate this system in rats implanted with electrodes in motor cortex and hippocampus and show how it can be used to assess relationships between reaching, pulling, and grasping movements and single-unit and local-field activity. Furthermore, we found that automating the shaping procedure significantly improved performance over manual training, with rats pulling > 100 m during a 15-minute session. COMPARISON WITH EXISTING
methodsString-pulling is typically shaped by tying food reward to the string and visually scoring behavior. The system described here automates training, streamlines video assessment with deep learning, and automatically segments reaching movements into distinct reach/pull phases. No system, to our knowledge, exists for the automated shaping and assessment of this behavior.
conclusionsThis system will be of general use to researchers investigating motor control, motivation, sensorimotor integration, and motor disorders such as Parkinson's disease and stroke.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.