Scalar variables, e.g., the orientation of a shape in an image, are commonly predicted using a single output neuron in a neural network. In contrast, the mammalian cortex represents variables with a population of neurons. In this population code, each neuron is most active at its preferred value and shows partial activity for other values. Here, we investigate the benefit of using a population code for the output layer of a neural network. We compare population codes against single-neuron outputs and one-hot vectors. First, we show theoretically and in experiments with synthetic data that population codes improve robustness to input noise in networks of stacked linear layers. Second, we demonstrate the benefit of using population codes to encode ambiguous outputs, such as the pose of symmetric objects. Using the T-LESS dataset of feature-less real-world objects, we show that population codes improve the accuracy of predicting 3D object orientation from image input.

翻译：标量变量（例如图像中形状的方向）通常使用神经网络中的单个输出神经元进行预测。相比之下，哺乳动物皮层使用神经元群体来表示变量。在这种群体编码中，每个神经元在其偏好值处最为活跃，并对其他值表现出部分活性。本文研究了在神经网络输出层使用群体编码的优势。我们将群体编码与单神经元输出和独热向量进行了比较。首先，我们在理论上和合成数据实验中证明，对于由堆叠线性层构成的网络，群体编码能够提高对输入噪声的鲁棒性。其次，我们展示了使用群体编码表示模糊输出（例如对称物体的姿态）的益处。通过使用无纹理真实物体数据集T-LESS，我们证明了群体编码能够提高从图像输入预测三维物体方向的准确性。