A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding
Machine Learning
2018-10-23 v1 Artificial Intelligence
Neurons and Cognition
Machine Learning
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.
Cite
@article{arxiv.1810.09391,
title = {A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding},
author = {Constantine Dovrolis},
journal= {arXiv preprint arXiv:1810.09391},
year = {2018}
}
Comments
Under peer-review