Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network

• URL: http://arxiv.org/abs/2208.01416v2
• Date: Wed, 28 Feb 2024 04:06:35 GMT
• Title: Biologically Plausible Training of Deep Neural Networks Using a Top-down Credit Assignment Network
• Authors: Jian-Hui Chen, Cheng-Lin Liu, Zuoren Wang
• Abstract summary: Top-Down Credit Assignment Network (TDCA-network) is designed to train a bottom-up network using a Top-Down Credit Assignment Network (TDCA-network) TDCA-network serves as a substitute for the conventional loss function and the back-propagation algorithm, widely used in neural network training. The results indicate TDCA-network holds promising potential to train neural networks across diverse datasets.
• Score: 32.575847142016585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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