  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 backpropagation (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.
