This paper introduces a novel transfer-learning mechanism for Neural Cellular Automata (NCAs) that injects a pretrained teacher's hidden states into a student model to guide early optimization. This results in superior generalization with a minimal parameter budget. The hidden channels of NCAs decouple feature extraction from uniform classification consensus, allowing for robust, decentralized computational substrates for parameter-efficient transfer learning.