Advancing Machine Intelligence with Neuroscience

Numenta’s deep experience in theoretical neuroscience research has led to tremendous discoveries about how the brain works. We have developed a framework called the Thousand Brains Theory of Intelligence that will be fundamental to advancing the state of artificial intelligence and machine learning. By applying this theory to existing deep learning systems, we are addressing today’s bottlenecks while enabling tomorrow’s applications.

We are a team of scientists and engineers applying neuroscience principles to machine intelligence research.

Our Dual Mission

To reverse-engineer the neocortex and to enable machine intelligence technology based on cortical theory.

Our Vision

We believe the brain is the best example of an intelligent system. The brain’s center of intelligence, the neocortex, controls a wide range of functions using a common set of principles. 

Today’s machine learning and AI have accomplished many impressive tasks but are restricted to narrow, specific goals. In stark contrast, intelligent machines continuously learn patterns in their environment without supervision, enabling them to tackle problems in entirely new ways. Intelligent machines that learn will have an enormous beneficial impact in the coming decades, and the neocortex provides the blueprint for building them.

Our Progress

Numenta has developed a novel theory and broad framework for understanding what the neocortex does and how it does it, called the Thousand Brains Theory of Intelligence. We have built a robust roadmap that lays out the steps to achieving truly intelligent machines based on the key principles of the Thousand Brains Theory.

Our Unique Approach

We are one of the few teams that has developed large-scale theories of the brain that are biologically constrained, testable, and implemented in software.

Our deep neuroscience research provides the foundation for delivering transformational changes in machine learning and AI. We document our research in several ways, including peer-reviewed journal papers, conference proceedings, and invited talks. In addition, we place our research commits in an open-source project. We strive to be completely open in everything we do.

Close Section

Our neuroscience research has uncovered a number of core principles that are not reflected in today’s machine learning systems. These principles have the promise to solve many of the known problems today’s machine learning and AI systems face. By incorporating these principles, we can overcome today’s limitations and build tomorrow’s intelligent machines.


What’s New in Research – Two Papers Available

Play Video

Subutai explains Numenta’s biological approach to machine intelligence and current work on sparsity

We recently released two research papers detailing how the principles of The Thousand Brains Theory lead to the development of more efficient and robust machine learning systems.

Avoiding Catastrophe: Active Dendrites Enable Multi-Task Learning in Dynamic Environments – Frontiers in Neurorobotics

  • We investigate if biologically inspired neurons can lead to a general solution to catastrophic interference. We found that active dendrites and sparse representations work together to mitigate catastrophic interference in dynamic settings.

Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks 

  • We introduce Complementary Sparsity, a novel technique that significantly improves the performance of dual sparse networks on existing hardware. We demonstrate that we can achieve high performance running weight-sparse networks, and we can multiply those speedups by incorporating activation sparsity.

Our Current Research Projects

Performance improvements in sparse networks

Representations in the brain are highly sparse, resulting in an extremely efficient system. For machine learning, sparsity also offers the promise of significant computational benefits, but most hardware architectures are not optimized for extreme sparsity. These limitations have hindered research into sparse models. Along with our hardware partners, we are developing methods for dramatically improving the computational efficiency of sparse neural networks.

Dynamic sparse networks

In the brain, cortical networks are sparsely connected and extremely dynamic. As many as 30% of the connections in the neocortex turn over every few days. We are investigating ways to create highly sparse networks that learn their structure dynamically through training.


Our neuroscience research has shown that sparse representations are more robust and stable than dense representations. We have developed a cortically inspired sparse algorithm that can be applied to deep learning networks trained through backpropagation. These networks have both sparse activations and sparse connections. Sparse networks achieve accuracy competitive with the state of the art dense models, but are significantly more robust to noise.

Continuous learning

Truly intelligent machines must have the capability to learn and adapt continuously, a property that is absent in today’s deep learning systems. In the brain, sparse representations plus a more complex neuron model, enables us to continuously learn new patterns in an unsupervised manner. Incorporating these ideas into artificial neural systems can enable systems that learn continuously from streaming data without any manual interventions.

Future Research Projects

Long term, our focus is on enabling the creation of truly intelligent machines that understand the world.  Future research projects may include:

  • Learning with much smaller training sets
  • Invariant representations and fast learning
  • Improving generalization
  • Building integrated sensorimotor systems that can plan, act and learn

Close Section

Our Goal

Vernon Mountcastle  proposed that because every part of the neocortex has the same complex circuitry, then every part is doing the same thing. Therefore, if we can understand a cortical column, we will understand the neocortex.

Through our focus on cortical theory, we seek to understand the complex circuitry of a cortical column. We want to understand the function and operation of the laminar circuits in the brain, including what the neurons are doing, what the layers are doing, how they interact, and how a cortical column works.

Video: Key Discoveries in Understanding How the Brain Works
Play Video

Key Discoveries in Understanding 
How the Brain Works

Our Approach

We are often asked how we do neuroscience research at Numenta and what it means to focus on cortical theory. We read many neuroscience papers and published results to get a deep understanding of neuroanatomy, neurophysiology, and cortical function. We propose theories about how the detailed architecture of the neocortex implements these functions. We ensure our work satisfies biological constraints and test our theories via simulation. Since we do not do any wet lab work, we collaborate with experimentalists that run investigations and give us feedback. 

We document our research in several ways, including peer-reviewed journal papers, conference proceedings, research reports, and invited talks. In addition, we place our daily research commits in an open source project  and answer questions about our research posted on our forums . We strive to be completely open in everything we do. 

Cortical Theory Our Approach

Our Theories

Although there is much to do, we have made significant progress on filling in the pieces of the common cortical circuit that underlines all of intelligence. We have proposed a novel theory and broad framework for understanding what the neocortex does and how it does it. We have made discoveries on how neurons make predictions, the role of dendritic spikes in cortical processing, how cortical layers learn sequences, and how cortical columns learn to model objects through movement.

Play Video

Our Research Focus Areas

Cortical Columns

Building on Mountcastle’s proposal that the neocortex is made up of nearly identical cortical columns, we are working on filling in the pieces of the cortical circuit – understanding each layer, what the neurons are doing, and how a cortical column works. As part of this research, we have proposed a framework, consistent with anatomical and physiological evidence, that explains how cortical columns function. The framework is based on how cortical columns learn sensorimotor models of the world by combining sensory inputs with location signals.

Find more information on cortical columns.

Cortical Theory Cortical Columns

Sequence Learning

Memory and recall of sequences is an essential component of inference and behavior. We believe that sequence memory is occurring in multiple layers of the neocortex. We’ve shown how a layer of pyramidal neurons with active dendrites, arranged in mini-columns, will learn transitions of patterns and form a robust sequence memory.

Find more information on sequence learning and prediction in cortex.

Cortical Theory - Sequence Learning

Sparse Distributed Representations

Sparse Distributed Representations (SDRs) are a foundational aspect of all of our theories. Everywhere in the neocortex, information is represented by distributed and sparsely active sets of neurons. We have shown through mathematical analysis and simulation that SDRs enable semantic generalization and robustness.

Find more information on sparse distributed representations.

Cortical Theory - Sparse Distributed Representations

Close Section

We have a growing collection of published peer-reviewed papers, supplemental white papers and research manuscripts. You can search our publications by category or by year. Some are currently under review at journals/conferences, but we have made all manuscripts freely available on preprint sites, such as arXiv or bioRxiv. Our goal is to document all of our discoveries in scientific journals.

  • Research papers • View and download the papers we have published and posted on preprint servers.
  • Research meeting videos • Watch the recordings of our research meetings.
  • Conference posters • View and download the posters we have presented at academic events.
  • Outside research • See how other scientists have analyzed our work in these papers that feature our research and technology.
Numenta Research Publications

Close Section


HTM (Hierarchical Temporal Memory) is an algorithmic implementation of the Thousand Brains Theory. HTM builds models of objects and makes predictions using sensory input. It generates motor commands to interact with its surroundings and continuously test its predictions. This continuous testing allows HTM to update its predictive models, and thus its knowledge. 

Learn more about our HTM technology, from application to forums, and more here.

Video: Intro to Our Technology
Play Video

Intro to Our Technology

Open Source

Because we want our technology to be broadly adopted, we make it widely accessible in an open-source project. You’ll find our algorithms, source code, and our latest work on applying HTM to today’s machine learning platforms there. For example, we’ve created libraries to create sparse deep learning networks in nupic.torch.

Anyone is welcome to use our technology for free, under the AGPLv3 open source license. If you are interested in seeing, developing or working with our technology, you’ll first have to sign the Contributor License. For more on our licenses, see the Licensing & Partners section.

We have an active discussion forum with HTM community members covering a variety of topics. We welcome members of the HTM community who want to translate our documentation into languages other than English.

Numenta Open Source Hackathon Event

Close Section

To help you learn about our theory and technology, we have created a number of videos, podcasts and educational resources. They are designed for anyone who wants to learn more about our cortical theory and machine intelligence research. 

  • Videos From keynote presentations to invited talks to cortical animations, view our library of videos to see our research developments firsthand.
  • Numenta On Intelligence Podcast Our podcast is about intelligence – how it works in the brain, what the key principles are, and how to apply those principles to machine learning systems.
  • HTM Forum • If you’re interested in discussing our work, or developing and implementing our technology, join our open source discussion forum.
  • Biological and Machine Intelligence (BAMI) This book (Biological And Machine Intelligence) documents many of the fundamental concepts of our theory and algorithms as of 2016. 
  • Books by Jeff Hawkins • Read the books where Jeff shares many of his research discoveries that led to our theory of intelligence and how that theory will impact the future of neuroscience and AI.
Play Video

Jeff speaking at MIT Technology Review’s EmTech Conference 2021 on Building a Better AI

Close Section

Numenta’s licensing and intellectual property (IP) strategy is to create an active research community and build a foundation for future intelligent applications. Unless it involves proprietary information regarding our work with other companies, we openly publish our scientific findings, software, intellectual property, and business strategy.

Open Source Technology & Scientific Use

GNU Affero Public License v3

We have several software libraries available for anyone to use, along with the associated IP, under an AGPLv3 license at no cost.

Our newer work, applying our cortical ideas to machine learning, can be found in nupic.torch. Our legacy work, applicable to anomaly detection and prediction on streaming data, can be found in nupic.

Contributor License

If you are interested in seeing, developing, or working with our technology, you’ll first have to sign the Contributor License.

Scientific Use

For scientists and researchers who want to use our intellectual property without our software, or whose work may be covered by our patents, we make a clear statement of non-assertion: as long as your work is for non-commercial use, we will not assert our patents.


We have a growing list of more than 40 U.S. and international patents that we believe will be critical for machine intelligence. The list of issued U.S. patents can be found here. In addition, we have pending U.S. and international patents that are not included in this list.

Commercial Deployment

Commercial licenses are currently only available under the terms of the AGPLv3 license. Although Numenta previously offered non-AGPL commercial licenses, we have discontinued that practice. The work we were licensing is now many years old and we are not maintaining it.  Our new technology, pertaining to machine learning, is not yet available for licensing. We will announce a commercial licensing program when available.

If you are interested in using our intellectual property without our software in a commercial application, or have questions about our patents and licensing, contact us at


Numenta partners bring the power of HTM to the market, bringing deep domain knowledge to our algorithms to add an application layer tuned to market needs. Learn more about our partners here

Numenta Licensing and Partners

Close Section


We may be busy contemplating cortical theory, but we’ve got a work-hard, play-hard attitude. At the heart of the peninsula, our downtown Redwood City location is a short walk from the Caltrain station. When we are in the office, our kitchen is stocked with snacks, and we enjoy weekly catered lunches from a variety of local restaurants. Outside the office, we enjoy getting together for company events, happy hours, and other fun activities. In the past, we’ve cheered on the SF Giants, baked pies at Pie Ranch in Pescadero, and do-si-doed through the night.

In addition to our full-time positions, we are always looking for strong research candidates to join us through our research internships.

Numenta Work Environment

Numenta is based in the San Francisco Bay Area with a physical office in downtown Redwood City.  We are currently requesting that employees work from the office on Tuesday and Wednesday. The remaining days, employees can choose to work from the office or from home.  Although we favor employees who are based in the Bay Area, we can make exceptions.  In those cases, we ask that employees travel to the Bay Area at least two times per year at specified times.  The Numenta office is restricted to people who have been fully vaccinated against COVID-19.  Prospective employees should be prepared to submit documentation of vaccination.

Numenta Door

Management Team

Donna Dubinsky*

CEO & Co-Founder
Jeff Hawkins

Jeff Hawkins

Subutai Ahmad

Subutai Ahmad

VP of Research and Engineering

Celeste Baranski


Christy Maver

VP of Marketing

Board of Directors

Ed Colligan

Former President & CEO, Palm, Inc.

Donna Dubinsky*

CEO & Co-Founder

Mike Farmwald

General Partner, Skymoon Ventures

Jeff Hawkins


Harry Saal

Chairman, Retrotope, Inc.

Close Section

Want to get in contact with us? Send us a message by filling out this form or use the appropriate email.

Fill out my online form.

Press Contact:

Licensing Inquiries:


Phone 650-369-8282
Fax 650-369-8283
Address 889 Winslow Street, 4th Floor
Redwood City, CA 94063
Map Google Maps Link
  • Reception is on the fourth floor
  • Street parking is available on surrounding streets
  • The Jefferson Avenue Garage is the nearest parking structure


Close Section

Donna Dubinsky

CEO & Co-Founder

*On temporary assignment at U.S. Department of Commerce

Donna is a serial entrepreneur best known for her work as CEO of Palm Computing and then Handspring, pioneers of the first successful handheld computers and smartphones. Previously, Donna spent 10 years in a multitude of sales, sales support, and logistics functions—both at Apple and at Claris, an Apple software subsidiary. She founded Numenta with her long-time business partner, Jeff Hawkins, in 2005.

Donna earned a B.A. from Yale University, and an M.B.A. from Harvard Business School. In addition to chairing Numenta’s board, she currently serves on the board of  Twilio (NYSE: TWLO). Donna also served on the board of Yale University from 2006-2018, including two years as Senior Fellow.

Jeff Hawkins


Jeff is a scientist whose life-long interest in neuroscience led to the creation of Numenta and its focus on neocortical theory. His research focuses on how the cortex learns predictive models of the world through sensation and movement. In 2002, he founded the Redwood Neuroscience Institute, where he served as Director for three years. The institute is currently located at U.C. Berkeley. Previously, he co-founded two companies, Palm and Handspring, where he designed products such as the PalmPilot and Treo smartphone. Jeff has written two books, On Intelligence (2004 with Sandra Blakeslee) and A Thousand Brains: A New Theory of Intelligence (2021).

Jeff earned his B.S. in Electrical Engineering from Cornell University in 1979. He was elected to the National Academy of Engineering in 2003.

Subutai Ahmad

VP of Research and Engineering

Subutai brings experience across real time systems, computer vision and learning to Numenta. He has previously served as VP Engineering at YesVideo, Inc. where he helped grow the company from a three-person start-up to a leader in automated digital media authoring. In 1997, Subutai co- founded ePlanet Interactive, a spin-off from Interval Research. ePlanet developed the IntelPlay Me2Cam, the first computer vision product developed for consumers. He has served as a key researcher at Interval Research.

Subutai holds a B.S. in Computer Science from Cornell University, and a Ph.D in Computer Science from the University of Illinois at Urbana-Champaign. While pursuing his Ph.D, Subutai completed a thesis on computational neuroscience models of visual attention.

Celeste Baranski


Celeste has vast experience in high tech engineering, design and management. Previously, Celeste was SVP Engineering & Operations at Panasas, a Big Data storage provider. She was CEO and Co-Founder of Vitamin D, one of the first developers to use the NuPIC platform. Celeste also served in VP Engineering roles at Palm and at Handspring, where she led the companies’ engineering efforts for handheld computers and smartphones. Celeste has built effective engineering teams, starting from a few to over 500 and has delivered numerous successful and award-winning products.

Celeste holds both a B.S. and M.S. in electrical engineering from Stanford University.

Christy Maver

VP of Marketing

Christy brings more than two decades of technology marketing and communications experience to Numenta. Previously, she launched analytics programs for the Retail and Healthcare industries as the Global Product Marketing Director of Analytics at Actian. Christy held a number of software marketing roles during her 13 years at IBM, where she managed user groups, produced live demos and developed big data video tutorials. She was also one of the founding members of IBM’s thought leadership group: the IBM Institute for Business Value.

Christy holds a BA in Economics from Princeton University.

Ed Colligan

Former President & CEO, Palm, Inc.

Ed has been a part of the core team of five Silicon Valley start-ups. Ed’s first big success was Radius, Inc. where he was instrumental in building products and the brand. After Radius, Ed was the first vice president of marketing for Palm where he helped develop the original Palm Pilot, the Palm brand and Palm’s product strategy. He moved on from Palm to found Handspring where Ed and his partners created the forbearer of all future smartphones; the Handspring Treo. Ed drove the transaction that reunited Palm and Handspring into a single Palm again. He established relationships with wireless carriers globally and drove Palm’s annual smartphone business to more than $2 billion. As the CEO of Palm, Ed spearheaded the transformation that created the WebOS platform and Palm Pre line of smartphones.

Ed now spends his time investing in and mentoring entrepreneurs. Ed is a board member of Numenta, Inc., Active Mind Technology, and POPS Worldwide, and is an investor and on the board of advisors of six other start-up companies. Ed holds a B.S. from the University of Oregon.

Mike Farmwald

General Partner, Skymoon Ventures

Mike is a successful serial entrepreneur. He has founded many companies with breakthrough technologies including FTL, a super computing company that merged with MIPs, Epigram, which was acquired by Broadcom, Rambus and Matrix Semiconductor, a creator of 3D integrated circuits.

Mike currently sits on the board of Rambus (NASDAQ: RMBS). He is participating on the Numenta board as an individual, rather than as a representative of Skymoon Ventures. Mike holds a B.S. degree in Mathematics from Purdue University and a Ph.D. in Computer Science from Stanford University.

Harry Saal

Chairman, Retrotope, Inc.

In 2002, Dr. Harry J. Saal was chosen by the US Department of Justice to lead the Technical Committee charged with monitoring and enforcing the Microsoft Antitrust case, and he served as Chairman of the Committee through the May 2011 expiration of the Judgment.

Harry founded Nestar Systems, a pioneer in local area network systems, in 1978. In 1986, Harry became the founder and CEO of Network General Corporation, the first company wholly dedicated to the area of network diagnostics. From 1993 through 1995, Harry served as founding CEO and President of Smart Valley, Inc., a non-profit organization chartered to create a regional electronic community based on an advanced information infrastructure and the collective ability to use it.

Harry is active in philanthropy and community affairs, and serves on the board of the American Institute of Mathematics, among others. Harry holds a B.A., M.A. and Ph.D. in Physics from Columbia University.