A neural circuit model for a contextual association task inspired by recommender systems
Abstract
Behavioral data shows that humans and animals have the capacity to learn rules of associations applied to specific examples, and generalize these rules to a broad variety of contexts. This article focuses on neural circuit mechanisms to perform a context‐dependent association task that requires linking sensory stimuli to behavioral responses and generalizing to multiple other symmetrical contexts. The model uses neural gating units that regulate the pattern of physiological connectivity within the circuit. These neural gating units can be used in a learning framework that performs low‐rank matrix factorization analogous to recommender systems, allowing generalization with high accuracy to a wide range of additional symmetrical contexts. The neural gating units are trained with a biologically inspired framework involving traces of Hebbian modification that are updated based on the correct behavioral output of the network. This modeling demonstrates potential neural mechanisms for learning context‐dependent association rules and for the change in selectivity of neurophysiological responses in the hippocampus. The proposed computational model is evaluated using simulations of the learning process and the application of the model to new stimuli. Further, human subject behavioral experiments were performed and the results validate the key observation of a low‐rank synaptic matrix structure linking stimuli to responses.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 14, 2020
- Source ID
- 10.1002/hipo.23194
Entities
People
- Allen Chang
- Chantal E. Stern
- Henghui Zhu
- Ioannis Ch. Paschalidis
- Michael Hasselmo
Organizations
- Boston University
- National Institute of General Medical Sciences
- National Science Foundation
- Office of Naval Research