A Framework for Intelligence and Cortical Function Based on Grid Cells in the Neocortex

• Main Authors: Jeff Hawkins, Marcus Lewis, Mirko Klukas, Scott Purdy, Subutai Ahmad
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-01-01
• Series: Frontiers in Neural Circuits
• Subjects: neocortex; grid cell; neocortical theory; hierarchy; object recognition; cortical column
• Online Access: https://www.frontiersin.org/article/10.3389/fncir.2018.00121/full

How the neocortex works is a mystery. In this paper we propose a novel framework for understanding its function. Grid cells are neurons in the entorhinal cortex that represent the location of an animal in its environment. Recent evidence suggests that grid cell-like neurons may also be present in the neocortex. We propose that grid cells exist throughout the neocortex, in every region and in every cortical column. They define a location-based framework for how the neocortex functions. Whereas grid cells in the entorhinal cortex represent the location of one thing, the body relative to its environment, we propose that cortical grid cells simultaneously represent the location of many things. Cortical columns in somatosensory cortex track the location of tactile features relative to the object being touched and cortical columns in visual cortex track the location of visual features relative to the object being viewed. We propose that mechanisms in the entorhinal cortex and hippocampus that evolved for learning the structure of environments are now used by the neocortex to learn the structure of objects. Having a representation of location in each cortical column suggests mechanisms for how the neocortex represents object compositionality and object behaviors. It leads to the hypothesis that every part of the neocortex learns complete models of objects and that there are many models of each object distributed throughout the neocortex. The similarity of circuitry observed in all cortical regions is strong evidence that even high-level cognitive tasks are learned and represented in a location-based framework.