Neuroscience and artificial intelligence (AI) both grapple with the challenge of adapting neural circuits to diverse tasks while maintaining efficiency and stability. Neuromodulatory systems in the brain dynamically regulate synaptic parameters to enable rapid, context-dependent reconfiguration, mirroring parameter-efficient fine-tuning (PEFT) techniques in deep learning. We propose that associative memory (AM) mechanisms can serve as a biologically plausible substrate for storing and retrieving task-specific modulatory signals, akin to adapter-based fine-tuning in AI models. Our framework integrates AM networks, such as Modern Hopfield Networks and Predictive Coding Networks, to store and recall PEFT-modulated weights, facilitating task adaptation in task-incremental and multi-task settings. Empirical results on Split-CIFAR100 and Split-TinyImageNet demonstrate that AMs can retrieve task-specific modulations with high fidelity, achieving comparable performance to disk-based storage. Our computational experiments show that storing these modulatory signals in AMs not only reduces the need for extensive synaptic rewiring but also sheds light on the neural basis of flexible task sets. By bridging neuromodulation and AI memory architectures, our work highlights a shared principle of task-dependent adaptation, offering insights into how the brain may reuse established circuits to meet evolving demands.