These are Jeff Hawkins' slides from the Computational Theories of the Brain Workshop held at the Simons Institute at UC Berkeley on April 17, 2018.
In this talk, I propose that the neocortex learns models of objects using the same methods that the entorhinal cortex uses to map environments. I propose that each cortical column contains cells that are equivalent to grid cells. These cells represent the location of sensor patches relative to objects in the world. As we move our sensors, the location of the sensor is paired with sensory input to learn the structure of objects. I explore the evidence for this hypothesis, propose specific cellular mechanisms that the hypothesis requires, and suggest how the hypothesis could be tested.
“A Theory of How Columns in the Neocortex Enable Learning the Structure of the World” by Jeff Hawkins, Subutai Ahmad, YuWei Cui (2017)
“Place Cells, Grid Cells, and the Brain’s Spatial Representation System” by Edvard Moser, Emilio Kropff, May-Britt Moser (2008)
“Evidence for grid cells in a human memory network” by Christian Doeller, Caswell Barry, Neil Burgess (2010)