Cortex-Inspired Continual Learning: Unsupervised Instantiation and Recovery of Functional Task Networks
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. Similar to mixture-of-experts, this method uses a high dimensional, self-organizing binary mask over a large population of small but deep networks, inspired by dendritic models of pyramidal neurons. The mask is produced by a three-stage procedure: (1) gradient descent on a continuous mask identifies task-relevant ne...
| Authors | Kevin McKee, Thomas Hazy, Yicong Zheng et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 April 2026 |
| Last updated at source | 29 April 2026 |
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