Yuwei Cui, Subutai Ahmad and Jeff Hawkins

Machine Learning, Sequence Learning

Published in Neural Computation (Peer-reviewed) • 2016/11/01

Machine Learning, Sequence Learning

Published in Neural Computation (Peer-reviewed) • 2016/11/01

This paper contains an analysis of HTM sequence memory applied to various sequence learning and prediction problems. Written with a machine learning perspective, the paper contains some comparisons to statistical and Deep Learning techniques.

Mirko Klukas, Marcus Lewis and Ila Fiete

Grid Cells, Neuroscience

Preprint of journal submission • 2019/03/16

Grid Cells, Neuroscience

Preprint of journal submission • 2019/03/16

This paper shows that a set of grid cell modules, each with only 2D responses, can generate unambiguous and high-capacity representations of variables in much higher-dimensional spaces.

Subutai Ahmad and Jeff Hawkins

Neuroscience, Sensorimotor, Sequence Learning

Preprint of journal submission • 2017/09/19

Neuroscience, Sensorimotor, Sequence Learning

Preprint of journal submission • 2017/09/19

This paper describes a cortical model for untangling sensorimotor from external sequences. It shows how a single neural mechanism can learn and recognize these two types of sequences: sequences where sensory inputs change due to external factors, and sequences where inputs change due to our own behavior (sensorimotor sequences).

Subutai Ahmad and Jeff Hawkins

Neuroscience, Sparse Distributed Representations

Preprint of journal submission • 2016/01/05

Neuroscience, Sparse Distributed Representations

Preprint of journal submission • 2016/01/05

This paper describes a mathematical model for quantifying the benefits and limitations of sparse representations in neurons and cortical networks.

Subutai Ahmad and Jeff Hawkins

Neuroscience, Sparse Distributed Representations

Research Paper • 2015/03/25

Neuroscience, Sparse Distributed Representations

Research Paper • 2015/03/25

An earlier version of the above submission, this paper applies our mathematical model of sparse representations to practical HTM systems.