This paper explains how location signals can be generated with a location layer that utilizes grid-cell-like neurons. It builds on our previous paper, A Theory of How Columns in the Neocortex Enable Learning the Structure of the World.
This paper proposes a network model composed of columns and layers that performs robust object learning and recognition. The model introduces a new feature to cortical columns, location information, which is represented relative to the object being sensed.
This foundational paper describes core HTM theory for sequence memory and its relationship to the neocortex. Written with a neuroscience perspective, the paper explains why neurons have so many synapses and how networks of neurons can form a powerful sequence learning mechanism.
This paper describes an important component of HTM, the HTM spatial pooler, which is a neurally inspired algorithm that learns sparse distributed representations online. Written from a neuroscience perspective, the paper demonstrates key computational properties of the HTM spatial pooler.
This paper demonstrates how Numenta’s online sequence memory algorithm, HTM, meets the requirements necessary for real-time anomaly detection in streaming data. It presents results using the Numenta Anomaly Benchmark (NAB), the first open-source benchmark designed for testing real-time anomaly detection algorithms.
