Proactive Adaptive Neighborhood-Based Dynamic Adjustment: A Brain-Region-Inspired Optimizer for Neural Network Optimization

Modern deep learning optimization faces persistent challenges in computational efficiency and convergence stability due to complex loss landscapes and heterogeneous parameter dynamics across architectures like CNNs, SNNs, and Transformers. While existing adaptive optimizers (e.g., AdamW, Lion) provide partial solutions, they inadequately address inter-parameter dependencies and neural adaptation mechanisms. Inspired by biological principles of synaptic plasticity and cortical organization, we propose PANDA (Parameter Adaptation through Neural Dynamics Alignment), a novel optimization framework that enhances convergence stability and model generalization through three key innovations: 1) Gradient similarity graphs modeling parameter interdependencies; 2) Dynamic importance quantification enabling context-aware parameter updates; and 3) Region-based grouping balancing local-global optimization dynamics. Comprehensive evaluations demonstrate PANDA's superiority over state-of-the-art methods, achieving 73.3% accuracy on CIFAR-100 with ResNet-50 (vs. AdamW's 70.4%) and reducing WikiText-103 perplexity to 9.1 in MLP fine-tuning (vs. Lion's 11.4). The framework exhibits particular strength in non-convex optimization, reducing gradient noise by 38% compared to baseline methods. While showing limitations in LoRA-based attention fine-tuning due to update granularity constraints, PANDA maintains robustness across diverse architectures. This work establishes biologically-inspired optimization as a viable paradigm for addressing fundamental challenges in deep learning. Future directions include adaptation mechanisms for attention-based architectures and computational efficiency improvements for large-scale deployment.