[论文解读] Modeling Retinal Ganglion Cell Population Activity with Restricted Boltzmann Machines

Matteo Zanotto, Riccardo Volpi+4 more|arXiv (Cornell University)|Jan 11, 2017

Neural dynamics and brain function参考文献 18被引用 7

一句话总结

本研究提出一种均值-协方差受限玻尔兹曼机（mcRBM），用于从高密度MEA记录中建模视网膜神经节细胞（RGC）群体活动，结果表明二值隐状态编码了与刺激相关的规律性及群体感受野。该方法能成功从RGC放电率中恢复视觉刺激，且在GABA受体阻断后互信息下降，验证了其生物学相关性。

ABSTRACT

The retina is a complex nervous system which encodes visual stimuli before higher order processing occurs in the visual cortex. In this study we evaluated whether information about the stimuli received by the retina can be retrieved from the firing rate distribution of Retinal Ganglion Cells (RGCs), exploiting High-Density 64x64 MEA technology. To this end, we modeled the RGC population activity using mean-covariance Restricted Boltzmann Machines, latent variable models capable of learning the joint distribution of a set of continuous observed random variables and a set of binary unobserved random units. The idea was to figure out if binary latent states encode the regularities associated to different visual stimuli, as modes in the joint distribution. We measured the goodness of mcRBM encoding by calculating the Mutual Information between the latent states and the stimuli shown to the retina. Results show that binary states can encode the regularities associated to different stimuli, using both gratings and natural scenes as stimuli. We also discovered that hidden variables encode interesting properties of retinal activity, interpreted as population receptive fields. We further investigated the ability of the model to learn different modes in population activity by comparing results associated to a retina in normal conditions and after pharmacologically blocking GABA receptors (GABAC at first, and then also GABAA and GABAB). As expected, Mutual Information tends to decrease if we pharmacologically block receptors. We finally stress that the computational method described in this work could potentially be applied to any kind of neural data obtained through MEA technology, though different techniques should be applied to interpret the results.

研究动机与目标

• 探究是否可利用概率建模从视网膜神经节细胞（RGC）的联合放电率分布中解码视觉刺激。
• 确定在均值-协方差受限玻尔兹曼机（mcRBM）中的二值隐变量是否能捕捉RGC群体活动中的刺激特异性规律。
• 通过评估其对抑制性GABA能神经回路药理学干扰的响应，评估所学隐状态的生理合理性。
• 检验模型在复杂、自然化刺激（如墙面动态视频帧）上的泛化能力。

提出的方法

• 使用高密度64×64多电极阵列（MEA）记录小鼠视网膜在视觉刺激下的动作电位活动。
• 利用对数高斯 Cox 过程从动作电位序列估计RGC放电率，以处理神经活动的不确定性。
• 应用均值-协方差受限玻尔兹曼机（mcRBMs）对连续放电率与二值隐变量的联合分布进行建模。
• 训练mcRBMs以学习捕捉RGC群体活动统计规律的隐表示。
• 通过计算隐状态与刺激之间的互信息（MI），量化编码性能。
• 通过比较GABA_C、GABA_A和GABA_B受体药理学阻断前后互信息的变化，评估模型的鲁棒性。

实验结果

研究问题

• RQ1 mcRBM中的二值隐状态是否能基于RGC群体活动编码不同的视觉刺激？
• RQ2 所学习的隐变量是否反映了如群体感受场等具有生物学意义的特性？
• RQ3 对GABA能抑制的药理学干扰如何影响隐状态编码视觉刺激的能力？
• RQ4 mcRBM模型能否泛化以编码复杂、自然化的视觉刺激，如动态墙面场景？

主要发现

• 在GABA受体阻断后，隐状态与视觉刺激之间的互信息显著下降，证实了模型对生理扰动的敏感性。
• 该模型能成功从RGC放电率分布中恢复刺激身份，且在正常视网膜条件下观察到更高的互信息。
• 隐单元编码了平滑的、类似刺激的特征，被解释为视网膜网络中群体感受场的表示。
• 从自然场景刺激中学到的隐状态编码了连贯的视觉特征，表明模型在简单光栅之外也具备泛化能力。
• 增加隐单元数量导致活动模式的平滑平均效果减弱，表明更高容量模型可分辨更精细的规律性。
• GABA阻断后，单个隐状态关联的刺激分布严重退化，表明群体编码的可靠性下降。