Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment
Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment
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DOI：
10.48550/arxiv.2310.20599
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发表时间：
2023-10
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影响因子：
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通讯作者：
Tahereh Toosi;Elias B. Issa
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作者：
Tahereh Toosi;Elias B. Issa
在自然视觉中，反馈连接支持多种视觉推理能力，例如理解被遮挡或嘈杂的自下而上的感觉信息，或调节纯粹的自上而下的过程，如想象。然而，反馈途径学习灵活地产生这些能力的机制尚不清楚。我们认为，自上而下的效应是通过前馈和反馈路径之间的对准来产生的，每个路径都优化了自己的目标。为了实现这种共同优化，我们引入了反馈-前馈对齐(FFA)，这是一种学习算法，它利用反馈和前馈路径作为相互信用分配计算图，从而实现对齐。在我们的研究中，我们在广泛使用的MNIST和CIFAR10数据集上展示了FFA在共同优化分类和重建任务方面的有效性。值得注意的是，FFA中的对齐机制赋予反馈连接紧急的视觉推理功能，包括去噪、解决遮挡、幻觉和想象。此外，与传统的反向传播(BP)方法相比，FFA在实现方面提供了生物合理性。通过将信用分配的计算图重新定位为目标驱动的反馈路径，FFA缓解了BP算法中遇到的权重传输问题，增强了学习算法的生物合理性。我们的研究将FFA作为一种有希望的概念验证，以了解视觉皮质中的反馈连接如何支持灵活的视觉功能。这项工作还有助于在感知现象下进行更广泛的视觉推理，并对开发更具生物启发的学习算法具有启示意义。
In natural vision, feedback connections support versatile visual inference capabilities such as making sense of the occluded or noisy bottom-up sensory information or mediating pure top-down processes such as imagination. However, the mechanisms by which the feedback pathway learns to give rise to these capabilities flexibly are not clear. We propose that top-down effects emerge through alignment between feedforward and feedback pathways, each optimizing its own objectives. To achieve this co-optimization, we introduce Feedback-Feedforward Alignment (FFA), a learning algorithm that leverages feedback and feedforward pathways as mutual credit assignment computational graphs, enabling alignment. In our study, we demonstrate the effectiveness of FFA in co-optimizing classification and reconstruction tasks on widely used MNIST and CIFAR10 datasets. Notably, the alignment mechanism in FFA endows feedback connections with emergent visual inference functions, including denoising, resolving occlusions, hallucination, and imagination. Moreover, FFA offers bio-plausibility compared to traditional back-propagation (BP) methods in implementation. By repurposing the computational graph of credit assignment into a goal-driven feedback pathway, FFA alleviates weight transport problems encountered in BP, enhancing the bio-plausibility of the learning algorithm. Our study presents FFA as a promising proof-of-concept for the mechanisms underlying how feedback connections in the visual cortex support flexible visual functions. This work also contributes to the broader field of visual inference underlying perceptual phenomena and has implications for developing more biologically inspired learning algorithms.