Understanding the neural basis of behavior is a fundamental goal in neuroscience. Current research in large-scale neuro-behavioral data analysis often relies on decoding models, which quantify behavioral information in neural data but lack details on behavior encoding. This raises an intriguing scientific question: ``how can we enable in-depth exploration of neural representations in behavioral tasks, revealing interpretable neural dynamics associated with behaviors''. However, addressing this issue is challenging due to the varied behavioral encoding across different brain regions and mixed selectivity at the population level. To tackle this limitation, our approach, named ``BeNeDiff'', first identifies a fine-grained and disentangled neural subspace using a behavior-informed latent variable model. It then employs state-of-the-art generative diffusion models to synthesize behavior videos that interpret the neural dynamics of each latent factor. We validate the method on multi-session datasets containing widefield calcium imaging recordings across the dorsal cortex. Through guiding the diffusion model to activate individual latent factors, we verify that the neural dynamics of latent factors in the disentangled neural subspace provide interpretable quantifications of the behaviors of interest. At the same time, the neural subspace in BeNeDiff demonstrates high disentanglement and neural reconstruction quality.

翻译：理解行为的神经基础是神经科学的核心目标。当前大规模神经行为数据分析的研究多依赖于解码模型，这类模型虽能量化神经数据中的行为信息，却缺乏对行为编码机制的深入阐释。这引出了一个关键的科学问题：“如何实现对行为任务中神经表征的深度探索，从而揭示与行为相关的可解释神经动力学？”然而，由于不同脑区行为编码模式的多样性以及群体水平的混合选择性，解决这一问题面临巨大挑战。为突破此局限，我们提出了名为“BeNeDiff”的方法：首先通过行为驱动的隐变量模型识别细粒度且解耦的神经子空间，随后利用前沿的生成扩散模型合成行为视频，以解释各隐因子对应的神经动力学。我们在包含背侧皮层宽场钙成像记录的多会话数据集上验证了该方法。通过引导扩散模型激活单个隐因子，我们证实了解耦神经子空间中隐因子的神经动力学能为目标行为提供可解释的量化表征。同时，BeNeDiff构建的神经子空间展现出高度的解耦特性和优异的神经信号重建质量。