  Despite the widespread adoption of Backpropagation algorithm-based Deep
Neural Networks, the biological infeasibility of the BP algorithm could
potentially limit the evolution of new DNN models. To find a biologically
plausible algorithm to replace BP, we focus on the top-down mechanism inherent
in the biological brain. Although top-down connections in the biological brain
play crucial roles in high-level cognitive functions, their application to
neural network learning remains unclear. This study proposes a two-level
training framework designed to train a bottom-up network using a Top-Down
Credit Assignment Network (TDCA-network). The TDCA-network serves as a
substitute for the conventional loss function and the back-propagation
algorithm, widely used in neural network training. We further introduce a
brain-inspired credit diffusion mechanism, significantly reducing the
TDCA-network's parameter complexity, thereby greatly accelerating training
without compromising the network's performance.Our experiments involving
non-convex function optimization, supervised learning, and reinforcement
learning reveal that a well-trained TDCA-network outperforms back-propagation
across various settings. The visualization of the update trajectories in the
loss landscape indicates the TDCA-network's ability to bypass local minima
where BP-based trajectories typically become trapped. The TDCA-network also
excels in multi-task optimization, demonstrating robust generalizability across
different datasets in supervised learning and unseen task settings in
reinforcement learning. Moreover, the results indicate that the TDCA-network
holds promising potential to train neural networks across diverse
architectures.
