LEARNING
具身神经计算：一种与缩放任务驱动验证接口的生物神经培养框架
Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler, Kiaran Lawson, Valentina Baccetti, Kwaku Dad Abu-Bonsrah, Candice Desouza, Finn Doensen, Bradley Watmuff, Daria Kornienko, Azin Azadi, Justin Leigh Bourke, Bernhard Sendhoff, Brett J. Kagan
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出了一种具身神经计算框架，用于优化生物神经网络与硅计算接口之间的编码/解码机制。通过大规模参数优化，在模拟网格世界中评估了约1300种参数组合，识别出12种能够持续学习气味梯度导航的配置。
关键词
embodied neurocomputationbiological neural networksencoding/decodingclosed-loop navigationparameter optimization
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