CRCNS: Data Sharing: A Joint Database of Experiments and Models of Reaching Movement
CRCNS: Data Sharing: A Joint Database of Experiments and Models of Reaching Movement
批准号:
1010336
负责人:
Konrad Kording
金额:
$42.5万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-09-15 至 2014-08-31
中文摘要
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英文摘要
Experiments in which subjects reach from one point to another are one of the workhorses of movement science. Many questions ranging from basic neuroscience (How do neurons represent movement?) to clinical (How do patients with Parkinson's Disease move differently from healthy controls?) to behavioral (How does loud noise lead to movement errors?) are studied using this paradigm. Moreover, many models have been constructed to describe reaching, and these models have strong links to robotics and computer science. This project will develop a database that contains both experimental results as well as models of reaching movement. The database will make it easier for experiments to falsify models and for models to be designed so that they overcome the limitations of previous models. The objective of the joint database design is that multiple models should be able to make predictions for a given experimental dataset, and likewise, multiple experimental datasets can be used to constrain a given model. The project will start with a relatively narrow set of experiments and models, widening the scope gradually over the course of the project. Research on reaching spans a broad set of questions and a broad set of experimental methods; however, many experimental approaches share significant aspects. The objective of the project is to enable inclusion of the results of a broad set of communities while keeping the database sufficiently coherent to be useful. To enable the inclusion of a broad set of participants who will share models and data the proposed project will include summer schools, workshops and competitions where participants can compare models. Access to the database will be free and open, and this possibility of access to high quality data promises to allow scientists from any movement related discipline to productively interact with movement data and models.
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