A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex
Abstract
The prefrontal cortex (PFC) enables humans’ ability to flexibly adapt to new environments and circumstances. Disruption of this ability is often a hallmark of prefrontal disease. Neural network models have provided tools to study how the PFC stores and uses information, yet the mechanisms underlying how the PFC is able to adapt and learn about new situations without disrupting preexisting knowledge remain unknown. We use a neural network architecture to show how hierarchical gating can naturally support adaptive learning while preserving memories from prior experience. Furthermore, we show how damage to our network model recapitulates disorders of the human PFC.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 05, 2020
- Source ID
- 10.1073/pnas.2009591117
Entities
People
- Ben Tsuda
- Hava T Siegelmann
- Kay M. Tye
- Terrence J. Sejnowski
Organizations
- National Science Foundation
- Office of Naval Research Global
- Salk Institute for Biological Studies
- University of California, San Diego
- University of Massachusetts Amherst