[论文解读] Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex

Jianghong Shi, Eric Shea‐Brown+1 more|arXiv (Cornell University)|Nov 18, 2019

Neural dynamics and brain function被引用 14

一句话总结

本研究基于艾伦脑图谱的数据，评估了人工神经网络与小鼠视觉皮层之间表征比较度量在有限神经元和刺激采样条件下的鲁棒性。研究发现，即使在采样受限的情况下，这些度量依然可靠，揭示了视觉皮层区域（尤其是V1）执行类似卷积网络深层的高阶、并行变换。

ABSTRACT

Partially inspired by features of computation in visual cortex, deep neural networks compute hierarchical representations of their inputs. While these networks have been highly successful in machine learning, it is still unclear to what extent they can aid our understanding of cortical function. Several groups have developed metrics that provide a quantitative comparison between representations computed by networks and representations measured in cortex. At the same time, neuroscience is well into an unprecedented phase of large-scale data collection, as evidenced by projects such as the Allen Brain Observatory. Despite the magnitude of these efforts, in a given experiment only a fraction of units are recorded, limiting the information available about the cortical representation. Moreover, only a finite number of stimuli can be shown to an animal over the course of a realistic experiment. These limitations raise the question of how and whether metrics that compare representations of deep networks are meaningful on these data sets. Here, we empirically quantify the capabilities and limitations of these metrics due to limited image and neuron sample spaces. We find that the comparison procedure is robust to different choices of stimuli set and the level of sub-sampling that one might expect in a large scale brain survey with thousands of neurons. Using these results, we compare the representations measured in the Allen Brain Observatory in response to natural image presentations. We show that the visual cortical areas are relatively high order representations (in that they map to deeper layers of convolutional neural networks). Furthermore, we see evidence of a broad, more parallel organization rather than a sequential hierarchy, with the primary area VisP (V1) being lower order relative to the other areas.

研究动机与目标

• 在现实实验约束条件下，评估人工神经网络与皮层数据之间表征比较度量的可靠性。
• 研究神经元子采样和有限刺激集对大规模脑图谱中这些度量有效性的影 响。
• 将这些鲁棒的度量应用于艾伦脑图谱的真实数据，比较皮层表征与深度神经网络特征。
• 确定小鼠视觉皮层区域相对于人工网络层的层级组织结构。
• 评估皮层表征是否更符合深度网络中的顺序层级结构，还是并行、分布式处理架构。

提出的方法

• 在不同刺激集和神经元子采样水平下，实证测试表征比较度量（如线性解码器、表征相似性分析）。
• 使用深度卷积神经网络（CNNs）作为分层视觉处理的计算模型。
• 将这些度量应用于艾伦脑图谱的在体记录数据，重点关注自然图像刺激下的反应。
• 基于表征相似性和解码性能，将皮层区域映射到CNN的对应层。
• 通过改变记录神经元数量和呈现刺激的多样性，评估度量的鲁棒性。
• 使用线性分类器从神经和网络表征中解码刺激身份，实现定量比较。

实验结果

研究问题

• RQ1 当神经元和刺激采样受限时，人工神经网络与皮层数据之间的表征比较度量有多鲁棒？
• RQ2 小鼠视觉皮层区域在表征相似性上最接近深度CNN的哪几层？
• RQ3 视觉皮层处理的组织方式是严格的层级序列，还是更接近并行、分布式架构？
• RQ4 初级视觉皮层（VisP/V1）在皮层层级中是否功能上处于较低层级？
• RQ5 在数据不完整的情况下，表征比较度量能否可靠揭示皮层区域的功能特性？

主要发现

• 即使仅使用部分神经元和刺激，表征比较度量依然保持鲁棒，支持其在大规模脑图谱研究中的应用。
• 视觉皮层区域（尤其是V1）映射到卷积神经网络的深层，表明其计算高阶表征。
• 数据表明视觉处理具有广泛的并行组织结构，而非严格的顺序层级。
• VisP（V1）在功能上相对于其他视觉区域处于较低层级，与其作为视觉处理初始皮层阶段的角色一致。
• 该比较框架在数据受限的情况下，成功识别出皮层区域与人工网络层之间的功能相似性。
• 结果支持将深度神经网络作为理解视觉系统皮层计算功能的模型。