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Abstract: We extend Memory-modulated Convolutional Neural Networks by introducing stochastic memory decay through multiple functions-linear, exponential, and logarithmic-replacing the earlier linear-only degradation. Each memory tensor undergoes random decay per inference, and its temporal distance is inferred via the relative change in mean activation. This approach enables the modeling of subjective time, where memories created simultaneously diverge in their perceived age due to differing decay paths. These results demonstrate that memory degradation can simulate temporal perception in CNNs, offering a biologically inspired foundation for time-aware neural computation.