Deep Learning and the Global Workspace Theory

Rufin VanRullen; Ryota Kanai

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arXiv · 2012.10390

Deep Learning and the Global Workspace Theory

Rufin VanRullen·Ryota Kanai

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Abstract

Recent advances in deep learning have allowed Artificial Intelligence (AI) to reach near human-level performance in many sensory, perceptual, linguistic or cognitive tasks. There is a growing need, however, for novel, brain-inspired cognitive architectures. The Global Workspace theory refers to a large-scale system integrating and distributing information among networks of specialized modules to create higher-level forms of cognition and awareness. We argue that the time is ripe to consider explicit implementations of this theory using deep learning techniques. We propose a roadmap based on unsupervised neural translation between multiple latent spaces (neural networks trained for distinct tasks, on distinct sensory inputs and/or modalities) to create a unique, amodal global latent workspace (GLW). Potential functional advantages of GLW are reviewed, along with neuroscientific implications.

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Rufin VanRullen, Ryota Kanai. 2020-12-04. Deep Learning and the Global Workspace Theory. https://arxiv.org/abs/2012.10390

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