Based on a wealth of neuroscience evidence, we have created HTM (Hierarchical Temporal Memory), a technology that is not just biologically inspired. It’s biologically constrained. When applied to computers, HTM algorithms are well suited for prediction, anomaly detection and ultimately sensorimotor applications. We believe this technology will be the foundation for the next wave of computing.
At the core of HTM are learning algorithms that can store, learn, infer and recall high-order sequences. Unlike most other machine learning methods, HTM algorithms learn time-based patterns in unlabeled data on a continuous basis. They are robust to noise, and high capacity, meaning they can learn multiple patterns simultaneously.
HTM algorithms work best with data that meets the following characteristics:
Our technology has been tested and implemented in software, all of which is developed with best practices and suitable for deploying in commercial applications.
HTM Studio is a free, desktop tool that lets you find real-time anomalies in your streaming data without having to program, code or set parameters.
Download HTM Studio and try it for yourself.
Because we want our technology to be broadly adopted, we make it widely accessible in an open source project. There you’ll find our algorithms, source code, and an active discussion forum with HTM community members covering a variety of topics.
You’ll also find our latest work with HTM applied to today’s machine learning platforms. For example, we’ve created libraries to create sparse Deep Learning Networks in nupic.torch. Our community has created an assortment of HTM implementations, experiments, and integrations available for study and use.
If you are interested in seeing, developing or working with our technology, we invite you to visit our HTM Community website .
Anyone is welcome to use our technology for free, under the AGPLv3 open source license . In addition, we have created a separate, trial license without commercial rights for those individuals or organizations who are unable to use the AGPLv3 license. For more on our licenses, see the Licensing & Partners section section.