Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments.

ABSTRACT: A key challenge for AI is to build embodied systems that operate in dynamically changing environments. Such systems must adapt to changing task contexts and learn continuously. Although standard deep learning systems achieve state of the art results on static benchmarks, they often struggle in dynamic scenarios. In these settings, error signals from multiple contexts can interfere with one another, ultimately leading to a phenomenon known as catastrophic forgetting. In this article we investigate biologically inspired architectures as solutions to these problems. Specifically, we show that the biophysical properties of dendrites and local inhibitory systems enable networks to dynamically restrict and route information in a context-specific manner. Our key contributions are as follows: firs

SUBMITTER: Iyer A 

PROVIDER: S-EPMC9100780 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature