Hybrid neural networks for continual learning inspired by corticohippocampal circuits.

ABSTRACT: Current artificial systems suffer from catastrophic forgetting during continual learning, a limitation absent in biological systems. Biological mechanisms leverage the dual representation of specific and generalized memories within corticohippocampal circuits to facilitate lifelong learning. Inspired by this, we develop a corticohippocampal circuits-based hybrid neural network (CH-HNN) that emulates these dual representations, significantly mitigating catastrophic forgetting in both task-incremental and class-incremental learning scenarios. Our CH-HNNs incorporate artificial neural networks and spiking neural networks, leveraging prior knowledge to facilitate new concept learning through episode inference, and offering insights into the neural functions of both feedforward and feedback loo

SUBMITTER: Shi Q 

PROVIDER: S-EPMC11788432 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature