发表机构
School of Life Sciences, Tsinghua University; School of Future Information Innovation, Fudan University（清华大学生命科学学院; 复旦大学未来信息创新学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SpikeSSL是基于动力学感知状态空间层的通用尖峰推断框架，通过多模态条件编码器与生物物理模拟数据增强，在跨钙指示剂的尖峰推断任务中实现了域内与零样本泛化的SOTA性能。
AI 中文摘要
双光子钙成像是在体记录大规模神经群体的标准工具，但在不断增多的各类钙指示剂中准确推断尖峰仍是未解决的问题。现有监督方法在域内准确率尚可，但对未见过的指示剂泛化性差，因为不同指示剂会产生不同的荧光动力学和信号统计特征，而现有架构仍是相对简单的通用时间回归器，缺乏与动力学匹配的归纳偏置。我们提出SpikeSSL，一种通用尖峰推断框架，其时间主干是一组双向IIR状态空间层，广泛基于钙动力学设计。多模态条件编码器将指示剂身份、采样率和轨迹级信号统计特征映射为全局条件向量，通过自适应层归一化调节主干，而异方差方差头提供校准后的逐帧不确定性。在由33个公开真值数据集构建的5个固定评估拆分基准上，SpikeSSL在域内和零样本留一指示剂设置中均达到了SOTA性能。我们还开发了一种生物物理模拟流水线，可生成具有系统变化的动力学参数、尖峰统计特征、响应非线性、基线漂移和噪声的配对荧光-尖峰轨迹，利用该流水线我们合成了约11000条模拟轨迹，用这些数据增强训练可有效缩小跨指示剂域差距并提升零样本泛化性。代码公开于此https URL。
英文摘要
Two-photon calcium imaging is a standard tool for recording large neural populations in vivo, yet inferring spikes accurately across the growing diversity of calcium indicators remains an open problem. Existing supervised methods achieve reasonable in-domain accuracy but generalize poorly to unseen indicators, because different indicators induce distinct fluorescence kinetics and signal statistics while existing architectures remain relatively simple generic temporal regressors without dynamics-matched inductive bias. We propose SpikeSSL, a universal spike inference framework whose temporal backbone is a bank of bidirectional IIR state-space layers broadly motivated by calcium dynamics. A multi-modal conditioning encoder maps indicator identity, sampling rate, and trace-level signal statistics into a global conditioning vector that modulates the backbone via Adaptive Layer Normalization, while a heteroscedastic variance head provides calibrated per-frame uncertainty. On a benchmark with five fixed evaluation splits built from 33 public ground-truth datasets, SpikeSSL achieves state-of-the-art performance in both in-domain and zero-shot leave-one-indicator-out settings. We also develop a biophysical simulation pipeline capable of generating paired fluorescence-spike traces with systematically varied kinetic parameters, spike statistics, response nonlinearities, baseline drift, and noise. Using this pipeline, we synthesize approximately 11,000 simulated traces. Augmenting training with these data effectively closes the cross-indicator domain gap and improves zero-shot generalization. Code is publicly available at https://github.com/detimage123/SpikeSSL.
