Metaplasticity dynamically adjusts how synaptic e[ffi]cacy and connectivity change, helping neural circuits adapt to experience. However, the interaction between changes in synaptic weight(W) and connection probability (P) remains poorly understood. We explored their interaction using a biologically-inspired, multi layer spiking neural network. We found that while W controls network excitability, P exerts layer-specific and time-dependent control, crucial for network stability. Simultaneous changes in W and P, i.e. metaplasticity, revealed complex, non-additive interactions, shaping response timing and neural recruitment, resulting in the emergence of functionally distinct neuronal subtypes: input-invariant neurons maintaining responsiveness and variant neurons enabling adaptation, based on di[ff]erential E-I dynamics. This interaction allows the network to achieve functional homeostasis in the inputlayer while preserving flexibility in superficial layers. We provide a novel framework for understanding how metaplasticity balances the competing demands of stability and adaptability in cortical circuits, with significant implications for learning, memory, and neural coding.