In the 1970’s the neuroscientist Vernon Mountcastle discovered that every region of the neocortex has a common repeating circuit, which he termed the “cortical column”. The startling implication is that every region must thus implement a common set of operations, regardless of sensory modality.
Our research is based on the premise that if we understand cortical columns, we will understand the neocortex at a very fundamental level. We have made significant progress on filling in the pieces of this common cortical circuit that underlines all of intelligence. The Thousand Brains Theory of Intelligence is our framework that outlines our discoveries on the function and operation of the circuits in the brain. Our goal is to translate these neuroscience discoveries to practical AI systems towards the goal of building truly intelligent systems.
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.
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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.
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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.
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