To help you learn about our theory and technology, we have organized educational content below. It is designed for anyone who wants to learn about HTM cortical theory and its applications for machine intelligence.
- Research Papers•Here you’ll find a collection of recent Numenta Research papers. Some of them are currently under review at journals/conferences, but we have made all manuscripts freely available on preprint sites, such as arXiv or bioRxiv.
- Biological and Machine Intelligence (BAMI)•This living book (Biological And Machine Intelligence) documents our Hierarchical Temporal Memory framework for both biological and machine intelligence.
- HTM School•This YouTube series is designed to educate the general public about Hierarchical Temporal Memory (HTM). Each 10-15 minute episode dives into a particular topic of HTM theory.
- AI Singapore Meetup: The Biological Path Toward Strong AIMatthew Taylor•Open Source Community Manager•2018/05/17
- Jeff Hawkins at the Simons Institute: Grid Cell-Like Mechanisms in the NeocortexJeff Hawkins•Co-founder•2018/04/17
- Jeff's MIT Talk: Have We Missed Half of What the Neocortex Does?Jeff Hawkins•Co-founder•2017/12/15
- ODSC West 2017: The Biological Path Toward Strong AIMatthew Taylor•Open Source Community Manager•2017/11/03
- How Columns in the Neocortex Enable LearningNumenta•Jeff Hawkins, Subutai Ahmad, and Yuwei Cui•2017/10/25
- Strange Loop: The Biological Path Toward Strong AIMatthew Taylor•Open Source Community Manager•2017/09/29
- Video Series: HTM Chat with Jeff HawkinsNumenta•Jeff Hawkins & Matt Taylor•2017/03/22
- CSV17: Reverse Engineering the Brain for Intelligent MachinesNumenta•Jeff Hawkins and Subutai Ahmad•2017/03/07
- Machine Intelligence with Streaming Data WebinarNumenta•Christy Maver & Scott Purdy•2016/04/26
- HTM Videos from Jeff HawkinsJeff Hawkins•Co-Founder•2014/11/22
- Applications of Hierarchical Temporal Memory (HTM)Chetan Surpur•Software Engineer•2014/10/17
- Getting Started With Numenta TechnologyNumenta•Celeste Baranski & Matt Taylor•2014/10/17
- Science of Anomaly DetectionScott Purdy•Engineering Manager•2014/10/17
- Sparse Distributed Representations: Our Brain's Data StructureSubutai Ahmad•VP Research•2014/10/17
- HTM Learning Algorithm Tutorial: Algorithm BasicsRahul Agarwal•Software Engineer•2013/08/03
- Jeff Hawkins on Loup Ventures Braintech Podcast SeriesDoug Clinton•Loup Ventures•2018/01/05
- Jeff Hawkins Explores a New Theory of Cortical FunctionGinger Campbell, MD•Brain Science Podcast•2017/11/27
- Hierarchical Temporal Memory (HTM) WhitepaperJeff Hawkins•Co-Founder•2011/09/12
- On Intelligence (Book) by Jeff HawkinsJeff Hawkins & Sandra Blakeslee•Co-Founder & Co-Author•2005/07/14
- Grid Cell Meeting 2018: Using Grid Cells for Coordinate TransformsNumenta Research Engineer•Marcus Lewis•2018/05/21
- Cosyne 2018: Determining Allocentric Locations of Sensed FeaturesNumenta•Marcus Lewis, Jeff Hawkins•2018/03/01
- Cosyne 2018: Sparse Distributed RepresentationsNumenta•Subutai Ahmad, Max Schwarzer, and Jeff Hawkins•2018/03/01
- CNS 2017: A Neural Mechanism for Sequence Learning – HTM Sequence MemoryNumenta•Subutai Ahmad, Yuwei Cui, Jeff Hawkins•2017/07/24
- NCM 2017: A Cortical Circuit for Sensorimotor Learning and RecognitionNumenta•Subutai Ahmad, Jeff Hawkins, Yuwei Cui•2017/05/01
- Cosyne 2017: How Do Cortical Columns Learn 3D Sensorimotor Models?Numenta•Jeff Hawkins, Yuwei Cui, Subutai Ahmad, Nathanael Romano, and Marcus Lewis•2017/02/24
- Bernstein Conference 2016: HTM Sequence Memory for Sequence LearningNumenta•Subutai Ahmad, Jeff Hawkins, Yuwei Cui•2016/09/21
- Cosyne 2016: Introducing HTM Sequence MemoryNumenta•Yuwei Cui, Subutai Ahmad, Jeff Hawkins, Chetan Surpur•2016/02/25
- Cosyne 2015: How The Cortex Builds a Sensorimotor Model of The WorldNumenta•Yuwei Cui, Subutai Ahmad, Jeff Hawkins, Chetan Surpur•2015/03/05
If you are interested in translating our materials, please see https://numenta.org/licenses/ for information about translations.