Diffusing Blame: Task-Dependent Credit Assignment in Biologically Plausible Dual-Stream Networks

2026-06-30

AI Summary

This work proposes a biologically plausible learning framework based on a dual-stream excitatory/inhibitory neural network that strictly adheres to Dale’s law—where each neuron’s synapses are exclusively excitatory or inhibitory—and achieves credit assignment without weight transposition through an error diffusion mechanism. The method incorporates modular error routing for multi-class classification, combined with layer-specific Sigmoid widths, batch-centered class-wise error signals, and asymmetric initialization to effectively alleviate credit assignment bottlenecks across diverse tasks. In supervised learning, the model attains 96.7% and 61.7% accuracy on MNIST and CIFAR-10, respectively. Furthermore, the proposed ED-PPO algorithm demonstrates competitive performance against current backpropagation-free baselines in Brax and Craftax reinforcement learning environments, confirming its scalability and generality.

Abstract

Biological neural circuits obey Dale's principle: each neuron's synapses are uniformly excitatory or inhibitory. Artificial networks that respect this constraint must coordinate separate excitatory and inhibitory populations, fundamentally changing how credit is assigned during learning. Several biologically plausible learning rules avoid backpropagation's weight transport requirement, but it has been difficult to achieve strong performance under Dale's principle beyond MNIST. Error Diffusion (ED) was originally proposed in a dual-stream excitatory/inhibitory architecture, where learning is driven by routing global error signals to all layers without transporting transposed forward weights or relying on random feedback matrices. Whether such a rule can scale under Dale's principle across both supervised classification and reinforcement learning remains unknown. Here, we introduce modulo error routing to extend Error Diffusion beyond binary classification, and show that a dual-stream excitatory/inhibitory architecture trained with this method achieves 96.7% on MNIST and establishes a 61.7% baseline on CIFAR-10, demonstrating that representation learning is possible even when strictly enforcing Dale's principle. For the classification setting, we introduce three domain-specific innovations: layer-specific sigmoid widths, batch-centered class error signals, and asymmetric initialization, and ablation analysis reveals that their relative importance reverses between MNIST and CIFAR-10, exposing task-dependent credit-assignment bottlenecks invisible to single-benchmark evaluation. In reinforcement learning, we integrate ED with Proximal Policy Optimization (PPO) and evaluate it on continuous-control tasks in Google Brax and on Craftax, an open-ended exploration task. We show that ED-PPO achieves competitive performance relative to Direct Feedback Alignment, a backpropagation-free baseline.

Problem

• credit assignment
• Dale's principle
• biologically plausible learning
• dual-stream networks
• error diffusion

Innovation

• Dale's principle
• Error Diffusion
• dual-stream network
• credit assignment
• biologically plausible learning