AI RESEARCH
Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks
arXiv CS.LG
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ArXi:2604.24637v1 Announce Type: new Block-sequential continual learning demands that a single model both protect prior solutions from catastrophic forgetting and efficiently infer at inference time which prior solution matches the current input without task labels. We present Functional Task Networks (FTN), a parameter-isolation method inspired by structural and dynamical motifs found in the mammalian neocortex.