Apr 28, 2020

Context-dependent extinction learning emerging from raw sensory inputs: A reinforcement learning approach

BioRxiv : the Preprint Server for Biology
T. WaltherSen Cheng

Abstract

The context-dependence of extinction learning has been well studied and requires the hippocampus. However, the underlying neural mechanisms are still poorly understood. Using memory-driven reinforcement learning and deep neural networks, we developed a model that learns to navigate autonomously in biologically realistic VR environments based on raw camera inputs alone. Neither is context represented explicitly in our model, nor is context change signaled. We find that memory-intact agents learn distinct context representations, and develop ABA renewal, whereas memory-impaired agents do not. These findings reproduce the behavior of control and hippocampal animals, respectively. We therefore propose that the role of the hippocampus in the context-dependence of extinction learning might stem from its function in episodic-like memory and not in context-representation per se. We conclude that context-dependence can emerge from raw visual inputs.

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Mentioned in this Paper

Genome
Metagenome
Extraction
Analysis
Molecular Analysis
Core
Silo (Dataset)
Population Group
Validation

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