Can Active Dendrites Mitigate Catastrophic Forgetting?

Human beings are remarkably robust at learning and retaining knowledge over time, despite the fact that the perceptual inputs we receive from the world are continuously changing. Unfortunately, the same cannot be said for artificial neural networks and this consequently restricts their use cases to settings where they're only required to perform a single task. In our new preprint titled " Going Beyond the Point Neuron: Active Dendrites and Sparse Representations for Continual Learning", we investigated how to augment neural networks with properties of real neurons, specifically active dendrites and sparse representations. Inspired by neuroscience findings, we hypothesized these mechanisms will permit knowledge retention over time. We found that these additions significantly enhanced a network's ability to learn sequentially, just as humans easily do.