利用生成扩散模型探索行为相关且解耦的神经动力学
Exploring Behavior-Relevant and Disentangled Neural Dynamics with Generative Diffusion Models
AI总结：
针对神经行为数据分析中解码模型缺乏行为编码细节的问题，提出BeNeDiff方法，通过行为感知潜变量模型识别解耦神经子空间，结合生成扩散模型合成行为视频解释潜因子动力学，在多会话数据集上验证了其高解耦性与重建质量。
摘要：
理解行为的神经基础是神经科学的基本目标。当前大规模神经行为数据分析的研究常依赖解码模型，这类模型量化神经数据中的行为信息，但缺乏行为编码的细节。这引出了一个有趣的科学问题：“如何深入探索行为任务中的神经表征，揭示与行为相关的可解释神经动力学”。然而，由于不同脑区的行为编码存在差异，且群体层面存在混合选择性，解决这一问题具有挑战性。为克服这一局限，我们提出名为“BeNeDiff”的方法，首先利用行为感知的潜变量模型识别细粒度且解耦的神经子空间，然后采用最先进的生成扩散模型合成行为视频，以解释每个潜因子的神经动力学。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潜因子，我们证实解耦神经子空间中潜因子的神经动力学可对目标行为提供可解释的量化。同时，BeNeDiff中的神经子空间表现出高解耦性和神经重建质量。
英文摘要：
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.
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。
