Learning dynamic representations of the functional connectome in neurobiological networks
Learning dynamic representations of the functional connectome in neurobiological networks
复制标题
DOI：
10.48550/arxiv.2402.14102
复制
发表时间：
2024-02
期刊：
影响因子：
--
通讯作者：
Luciano Dyballa;Samuel Lang;Alexandra Haslund-Gourley;Eviatar Yemini;Steven W. Zucker
中科院分区：
文献类型：
--
作者：
Luciano Dyballa;Samuel Lang;Alexandra Haslund-Gourley;Eviatar Yemini;Steven W. Zucker
神经元回路的静态突触连接与其功能的动态形成直接对比。正如在不断变化的社区互动中一样，不同的神经元可以积极参与各种组合，以影响不同时间的行为。我们引入了一种无监督的方法来学习活体行为动物神经元之间的动态亲和力，并揭示不同时间神经元之间形成的群落。推理分两个主要步骤进行。1 首先，通过非负张量分解 (NTF) 来组织全脑钙活动的神经元痕迹之间的成对非线性亲和力。每个因素都指定了哪些神经元组最有可能在推断的时间间隔内相互作用，以及针对哪些动物。最后，将允许加权群落检测的生成模型应用于 NTF 生成的功能基序，以揭示动态功能连接组。由于时间编码了不同的实验变量（例如，化学刺激的应用），因此这提供了在实验的不同阶段（例如，刺激应用或自发行为）期间活跃的神经基序的图谱。我们的分析结果经过实验验证，证实我们的方法能够稳健地预测神经元之间的因果相互作用以产生行为。
The static synaptic connectivity of neuronal circuits stands in direct contrast to the dynamics of their function. As in changing community interactions, different neurons can participate actively in various combinations to effect behaviors at different times. We introduce an unsupervised approach to learn the dynamic affinities between neurons in live, behaving animals, and to reveal which communities form among neurons at different times. The inference occurs in two major steps.1 First, pairwise non-linear affinities between neuronal traces from brain-wide calcium activity are organized by non-negative tensor factorization (NTF). Each factor specifies which groups of neurons are most likely interacting for an inferred interval in time, and for which animals. Finally, a generative model that allows for weighted community detection is applied to the functional motifs produced by NTF to reveal a dynamic functional connectome. Since time codes the different experimental variables (e.g., application of chemical stimuli), this provides an atlas of neural motifs active during separate stages of an experiment (e.g., stimulus application or spontaneous behaviors). Results from our analysis are experimentally validated, confirming that our method is able to robustly predict causal interactions between neurons to generate behavior.