Brain-like Flexible Visual Inference by Harnessing Feedback-Feedforward Alignment.

ABSTRACT: 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 reconst

SUBMITTER: Toosi T 

PROVIDER: S-EPMC10635293 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature