论文标题
无监督的深度学习可以识别单个颞下神经元中的语义分离
Unsupervised deep learning identifies semantic disentanglement in single inferotemporal neurons
论文作者
论文摘要
经过培训的对象进行分类的深度监督神经网络已成为灵长类动物腹流中的流行计算模型。这些模型代表具有高维分布式人群代码的信息，这意味着颞下降（IT）响应也太复杂了，无法在单神经元级别进行解释。我们通过使用深度无监督的生成模型Beta-vae来对其面孔的神经反应进行建模来挑战这种观点。与深层分类器不同，β-VAE将感觉数据“解开”到可解释的潜在因素（例如性别或头发长度）中。我们发现该模型发现的生成因子与单个IT神经元编码的生成因子之间存在显着的对应关系。此外，我们能够使用只有少数单元的信号来重建面部图像。这表明腹侧视觉流可能正在优化分离目标，从而产生一个在单单元级别上具有低维且语义上可解释的神经代码。
Deep supervised neural networks trained to classify objects have emerged as popular models of computation in the primate ventral stream. These models represent information with a high-dimensional distributed population code, implying that inferotemporal (IT) responses are also too complex to interpret at the single-neuron level. We challenge this view by modelling neural responses to faces in the macaque IT with a deep unsupervised generative model, beta-VAE. Unlike deep classifiers, beta-VAE "disentangles" sensory data into interpretable latent factors, such as gender or hair length. We found a remarkable correspondence between the generative factors discovered by the model and those coded by single IT neurons. Moreover, we were able to reconstruct face images using the signals from just a handful of cells. This suggests that the ventral visual stream may be optimising the disentangling objective, producing a neural code that is low-dimensional and semantically interpretable at the single-unit level.
