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Deep Neural Networks for Image-Based Dietary Assessment
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从使用深度循环网络的神经数据推断流场推断
Timothy Doyeon Kim1, Thomas Zhihao Luo1, Tankut Can2
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ.
bioRxiv : the preprint server for biology
|November 28, 2023
概括
研究人员使用深度反复网络 (FINDR) 来开发了神经数据的流场推断,这是一种无监督的深度学习方法,用于揭示神经群体动态. FINDR有效地捕捉复杂的神经活动,有助于理解大脑计算.
科学领域:
- 计算神经科学是一种计算神经科学.
- 机器学习应用于神经生物学.
背景情况:
- 了解神经群体动态对于破译诸如决策等大脑计算至关重要.
- 从神经数据中估计这些复杂的动态,这是一项重大的方法挑战.
结论:
- FINDR提供了一种强大的方法来发现低维,任务相关的神经动态.
- 这种方法推进了神经群体活动及其在认知计算中的作用的研究.
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