Open AccessA modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex
Author(s) -
Ben Tsuda,
Kay M. Tye,
Hava T. Siegelmann,
Terrence J. Sejnowski
Publication year - 2020
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2009591117
Subject(s) - prefrontal cortex , gating , computer science , neuroscience , artificial neural network , transfer of learning , artificial intelligence , machine learning , cognitive psychology , psychology , cognition
The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underlying schema encoding and storage. Yet how the brain is able to create new schemas while preserving and utilizing old schemas remains unclear. Here we propose a simple neural network framework that incorporates hierarchical gating to model the prefrontal cortex's ability to flexibly encode and use multiple disparate schemas. We show how gating naturally leads to transfer learning and robust memory savings. We then show how neuropsychological impairments observed in patients with prefrontal damage are mimicked by lesions of our network. Our architecture, which we call DynaMoE, provides a fundamental framework for how the prefrontal cortex may handle the abundance of schemas necessary to navigate the real world.