convSeq：用于检测尖峰数据中模式的快速可扩展方法
convSeq: Fast and Scalable Method for Detecting Patterns in Spike Data
AI总结：
针对大规模神经记录中重复时空尖峰模式缺乏高效可扩展检测方法的问题，提出无监督方法convSeq，通过反向传播优化时空滤波器识别神经模式，速度超现有方法，为自发神经活动分析树立新标准。
摘要：
自发神经活动在记忆、学习和空间导航中至关重要，通常表现为重复的时空模式。尽管其重要性颇高，但由于缺乏高效且可扩展的检测方法，在大规模神经记录中分析这些模式仍具挑战。为填补这一空白，我们提出convSeq——一种无监督方法，采用反向传播优化时空滤波器，可有效识别这些神经模式。我们在多种合成数据和真实神经记录上验证了该方法的性能，其以前所未有的可扩展性和效率识别出尖峰序列。convSeq在速度上显著超越现有方法，为分析自发神经活动树立了新标准，有望推动我们对神经回路信息处理的理解。
英文摘要：
Spontaneous neural activity, crucial in memory, learning, and spatial navigation, often manifests itself as repetitive spatiotemporal patterns. Despite their importance, analyzing these patterns in large neural recordings remains challenging due to a lack of efficient and scalable detection methods. Addressing this gap, we introduce convSeq, an unsupervised method that employs backpropagation for optimizing spatiotemporal filters that effectively identify these neural patterns. Our method's performance is validated on various synthetic data and real neural recordings, revealing spike sequences with unprecedented scalability and efficiency. Significantly surpassing existing methods in speed, convSeq sets a new standard for analyzing spontaneous neural activity, potentially advancing our understanding of information processing in neural circuits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
