Cortical processing, in vision and other domains, combines bottom-up (BU) with extensive top-down (TD) processing. Two primary goals attributed to TD processing are learning and directing attention. These two roles are accomplished in current network models through distinct mechanisms. Attention guidance is often implemented by extending the model's architecture, while learning is typically accomplished by an external learning algorithm such as back-propagation. In the current work, we present an integration of the two functions above, which appear unrelated, using a single unified mechanism inspired by the human brain. We propose a novel symmetric bottom-up top-down network structure that can integrate conventional bottom-up networks with a symmetric top-down counterpart, allowing each network to recurrently guide and influence the other. For example, during multi-task learning, the same top-down network is being used for both learning, via propagating feedback signals, and at the same time also for top-down attention, by guiding the bottom-up network to perform a selected task. In contrast with standard models, no external back-propagation is used for learning. Instead, we propose a 'Counter-Hebb' learning, which adjusts the weights of both the bottom-up and top-down networks simultaneously. We show that our method achieves competitive performance on standard multi-task learning benchmarks. Yet, unlike existing methods, we rely on single-task architectures and optimizers, without any task-specific parameters. The results, which show how attention-guided multi-tasks can be combined efficiently with internal learning in a unified TD process, suggest a possible model for combining BU and TD processing in human vision.

翻译：视觉及其他领域的皮层处理结合了自下而上（BU）与广泛的自上而下（TD）处理。自上而下处理的两个主要目标被认为是学习与引导注意力。在当前网络模型中，这两种功能通过不同的机制实现：注意力引导通常通过扩展模型架构实现，而学习通常通过外部学习算法（如反向传播）完成。在本研究中，我们提出将上述看似无关的两种功能整合为一种受人类大脑启发的统一机制。我们提出一种新颖的对称自下而上-自上而下网络结构，可将传统自下而上网络与对称的自上而下网络相结合，使每个网络能够循环引导并影响对方。例如，在多任务学习过程中，同一自上而下网络既通过传播反馈信号用于学习，又通过引导自下而上网络执行选定任务实现自上而下注意力。与标准模型不同，该方法无需外部反向传播进行学习，而是提出一种"逆赫布"学习规则，可同步调整自下而上与自上而下网络的权重。实验表明，该方法在标准多任务学习基准上取得了具有竞争力的性能。与现有方法不同，本方法依赖单任务架构和优化器，无需任何任务特定参数。这些结果展示了注意力引导的多任务如何通过统一的自上而下过程与内部学习高效结合，为人类视觉中BU与TD处理的协同建模提供了可行方案。