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arXiv · 1810.09391

A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding

Constantine Dovrolis
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Abstract

We propose that the Continual Learning desiderata can be achieved through a neuro-inspired architecture, grounded on Mountcastle's cortical column hypothesis. The proposed architecture involves a single module, called Self-Taught Associative Memory (STAM), which models the function of a cortical column. STAMs are repeated in multi-level hierarchies involving feedforward, lateral and feedback connections. STAM networks learn in an unsupervised manner, based on a combination of online clustering and hierarchical predictive coding. This short paper only presents the architecture and its connections with neuroscience. A mathematical formulation and experimental results will be presented in an extended version of this paper.

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Constantine Dovrolis. 2018-10-22. A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding. https://arxiv.org/abs/1810.09391

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