Remembrance of Tasks Past in Tunable Physical Networks

TL;DR

This paper proposes a threshold-based learning rule in tunable physical networks that enhances memory retention of multiple sequential tasks by limiting changes to significant edges, thus mitigating catastrophic forgetting.

Contribution

Introducing a thresholding mechanism in physical network learning rules to confine tuning and improve sequential task memory retention.

Findings

Enhanced memory of multiple tasks in resistor networks

Reduced number of edges and tuning cost

Effective partitioning of network into functional regions

Abstract

Sequential learning in physical networks is hindered by catastrophic forgetting, where training a new task erases solutions to earlier ones. We show that we can significantly enhance memory of previous tasks by introducing a hard threshold in the learning rule, allowing only edges with sufficiently large training signals to be altered. Thresholding confines tuning to the spatial vicinity of inputs and outputs for each task, effectively partitioning the network into weakly overlapping functional regions. Using simulations of tunable resistor networks, we demonstrate that this strategy enables robust memory of multiple sequential tasks while reducing the number of edges and the overall tuning cost. Our results hint at constrained training as a simple, local, and scalable mechanism to overcome catastrophic forgetting in tunable matter.