Closed-loop neuroscience experimentation, where recorded neural activity is used to modify the experiment on-the-fly, is critical for deducing causal connections and optimizing experimental time. A critical step in creating a closed-loop experiment is real-time inference of neural activity from streaming recordings. One challenging modality for real-time processing is multi-photon calcium imaging (CI). CI enables the recording of activity in large populations of neurons however, often requires batch processing of the video data to extract single-neuron activity from the fluorescence videos. We use the recently proposed robust time-trace estimator-Sparse Emulation of Unused Dictionary Objects (SEUDO) algorithm-as a basis for a new on-line processing algorithm that simultaneously identifies neurons in the fluorescence video and infers their time traces in a way that is robust to as-yet unidentified neurons. To achieve real-time SEUDO (realSEUDO), we optimize the core estimator via both algorithmic improvements and an fast C-based implementation, and create a new cell finding loop to enable realSEUDO to also identify new cells. We demonstrate comparable performance to offline algorithms (e.g., CNMF), and improved performance over the current on-line approach (OnACID) at speeds of 120 Hz on average.

翻译：闭环神经科学实验通过实时利用记录的神经活动动态调整实验方案，对于推断因果关联和优化实验时间至关重要。构建闭环实验的关键步骤是从流式记录中实时推断神经活动。多光子钙成像（CI）是实现实时处理具有挑战性的模态之一。CI能够记录大规模神经元群体的活动，但通常需要对视频数据进行批处理，才能从荧光视频中提取单个神经元的活动。我们以近期提出的鲁棒性时间轨迹估计器——稀疏模拟未使用字典对象（SEUDO）算法为基础，开发了一种新型在线处理算法，该算法能够同步识别荧光视频中的神经元并推断其时间轨迹，且对尚未识别的神经元具有鲁棒性。为实现实时SEUDO（realSEUDO），我们通过算法改进和基于C语言的快速实现优化了核心估计器，并创建了新的细胞检测循环机制，使realSEUDO能够同时识别新出现的细胞。实验证明，该算法在平均120 Hz的处理速度下，性能与离线算法（如CNMF）相当，且优于当前在线处理方法（OnACID）。