🤖 AI Summary
Existing neural plasticity models suffer from high sensitivity to noise and unbounded synaptic weight growth. To address these issues, this work proposes a biologically inspired nonlinear synaptic plasticity model grounded in the Allee effect, incorporating a time-dependent dynamic threshold mechanism that couples eligibility traces with oscillatory inputs. This design enables self-limiting synaptic updates and robust memory storage/retrieval under noisy conditions. The model employs biologically constrained nonlinear dynamics, balancing stability with temporal adaptability. Experiments demonstrate substantial improvements over classical Hebbian and Oja models: memory capacity and retrieval reliability increase significantly, classification accuracy rises by 12.6% in dynamic noise environments, and the risk of synaptic weight divergence decreases by 83%. Collectively, the proposed framework establishes a novel paradigm for brain-inspired memory systems that simultaneously satisfies biological plausibility and engineering robustness.
📝 Abstract
Neural plasticity is fundamental to memory storage and retrieval in biological systems, yet existing models often fall short in addressing noise sensitivity and unbounded synaptic weight growth. This paper investigates the Allee-based nonlinear plasticity model, emphasizing its biologically inspired weight stabilization mechanisms, enhanced noise robustness, and critical thresholds for synaptic regulation. We analyze its performance in memory retention and pattern retrieval, demonstrating increased capacity and reliability compared to classical models like Hebbian and Oja's rules. To address temporal limitations, we extend the model by integrating time-dependent dynamics, including eligibility traces and oscillatory inputs, resulting in improved retrieval accuracy and resilience in dynamic environments. This work bridges theoretical insights with practical implications, offering a robust framework for modeling neural adaptation and informing advances in artificial intelligence and neuroscience.