Expectation Learning for Adaptive Crossmodal Stimuli Association

TL;DR

This paper introduces a deep neural architecture that employs expectation learning to improve crossmodal stimuli association, enabling better generalization and adaptation in dynamic environments.

Contribution

It presents a novel expectation learning-based deep neural model for unsupervised crossmodal stimuli association, demonstrating self-adaptive behavior.

Findings

Model exhibits self-adaptable behavior

Enhances crossmodal generalization

Paves the way for adaptive deep learning architectures

Abstract

The human brain is able to learn, generalize, and predict crossmodal stimuli. Learning by expectation fine-tunes crossmodal processing at different levels, thus enhancing our power of generalization and adaptation in highly dynamic environments. In this paper, we propose a deep neural architecture trained by using expectation learning accounting for unsupervised learning tasks. Our learning model exhibits a self-adaptable behavior, setting the first steps towards the development of deep learning architectures for crossmodal stimuli association.