用于联合行为和神经分析的可学习潜在嵌入
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Cebra 特性 Cebra 是一种机器学习工具，使用非线性技术从同时记录的联合行为和神经数据中创建一致且高性能的潜在空间. 主要特点: 神经潜在嵌入: 用于假设检验和探索性分析. 验证准确性: 在钙信号和电生理数据集上验证了功效, 感官和运动任务, 跨物种的简单或复杂行为. 多会话与无标签: 可用于单会话或多会话数据集且无需标签. 高精度解码: 能够快速解码视觉皮层的自然电影. 代码可用性: Access the tool’s code on GitHub and read the pre-print on arxiv.org. 使用案例: • Analyze and decode behavioural and neural data to reveal underlying neural representations. • Map and uncover complex kinematic features in neuroscience research. • Produce consistent latent spaces across various data types and experiments. Cebra is a valuable tool for neuroscientists who wish to analyze and decode behavioural and neural data, allowing them to better understand the underlying neural representations involved in adaptive behaviours.