Low-Rank Adaptation (LoRA) is a widely used method to fine-tune large language models, extremely reducing computational and storage costs. But its fixed-rank design cannot well capture the varying importance across different layers, limiting its flexibility. Dynamic rank allocation methods mitigate this issue by adaptively allocating ranks during training, but most of them focus solely on either rank pruning or expansion, leading to redundant parameterization or insufficient representational capacity. To address this problem, we introduce Hebbian-Guided Bi-Directional Rank Adaptation (HeBiRA), a novel framework that bi-directionally reallocates low-rank capacity using Hebbian-inspired importance estimation. HeBiRA computes the contribution of each rank direction by measuring the synergy between activations and output gradients, and adjusts the rank bi-directionally by pruning uninformative directions while expanding those in critical layers. This mechanism flexibly redistributes the rank budget during training and also can be applied to PEFT methods such as DoRA, HiRA, and QLoRA. Experiments on multiple benchmarks and theoretical analysis show that HeBiRA consistently improves performance over baselines.