用自然视频和潜变量建模神经动态，揭示感官与行为的内在联系。
Modeling Dynamic Neural Activity by combining Naturalistic Video Stimuli and Stimulus-independent Latent Factors
- 融合视频刺激与独立潜变量，预测神经元响应联合分布。
- 在小鼠视觉皮层数据上，对数似然和相关性均优于纯视频模型。
- 潜变量隐含行为与皮层位置信息，无需标注即可发现生物意义结构。
视觉皮层神经活动受外部刺激和内部脑状态共同影响。现有动态编码模型未显式建模潜状态及完整神经响应分布。本文提出一种概率模型，基于自然视频刺激与刺激无关的潜因子，预测神经元响应的联合分布。在小鼠初级视觉皮层（V1）神经数据上训练测试后发现，该模型在对数似然上优于仅依赖视频的模型，并在条件于其他神经元响应时，提升似然与相关性。此外，学习到的潜因子与小鼠行为强相关，且呈现与神经元在视觉皮层位置相关的模式，尽管训练中未使用行为或皮层坐标信息。结果表明，从群体响应中无监督学习潜因子可揭示连接感官处理与行为的生物学有意义结构，无需训练时引入行为标注。
原文摘要 · Abstract (English)
The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. Currently, there are no dynamic encoding models that explicitly model a latent state and the entire neuronal response distribution. We address this gap by proposing a probabilistic model that predicts the joint distribution of the neuronal responses from video stimuli and stimulus-independent latent factors. After training and testing our model on mouse V1 neuronal responses, we find that it outperforms video-only models in terms of log-likelihood and achieves improvements in likelihood and correlation when conditioned on responses from other neurons. Furthermore, we find that the learned latent factors strongly correlate with mouse behavior and that they exhibit patterns related to the neurons' position on the visual cortex, although the model was trained without behavior and cortical coordinates. Our findings demonstrate that unsupervised learning of latent factors from population responses can reveal biologically meaningful structure that bridges sensory processing and behavior, without requiring explicit behavioral annotations during training.
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