Magnetic resonance imaging (MRI) provides excellent diagnostic detail but suffers from long scan times. Accelerated imaging through undersampling reduces acquisition but introduces aliasing, while parallel imaging and compressed sensing degrade quality at higher acceleration. Deep learning methods trained on large datasets suppress artifacts effectively, yet often over-smooth or hallucinate features. Implicit neural representations (INRs), which optimize continuous image functions per-instance, preserve fine details but remain under-constrained, leaving residual artifacts. We propose PrIINeR (prior-informed implicit neural representation) [1], a framework that unites population-trained priors with INR optimization. PrIINeR employs a hash-grid encoded implicit network jointly optimized with coil sensitivity maps and guided by a dual data consistency objective, enforcing fidelity to both undersampled k-space and prior-informed reconstructions. Total variation regularization further suppresses aliasing while maintaining sharp edges. On the NYU fastMRI [2] knee dataset (4–10× undersampling), PrIINeR consistently improves reconstruction quality over both INR-only methods and population-trained priors of varying complexity. Gains in SSIM and PSNR are statistically significant (p < 0.05) across nearly all settings, while qualitative results confirm better artifact suppression and preservation of anatomical detail. By combining global priors with instance-specific fidelity, PrIINeR offers a robust, flexible approach to accelerated MRI. Code is publicly available at: https://github.com/multimodallearning/PrIINeR.