We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement. We discuss the relationship of the model to cortical circuitry and suggest that the reciprocal connections between layers 4 and 6 fit the requirements of the model. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. Sensory input causes the network to invoke previously learned locations that are consistent with the input, and motor input causes the network to update those locations. Another layer of cells which processes sensory input receives this location input as context and uses it to encode the sensory input in the object’s reference frame. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. We describe a two-layer neural network model that uses cortical grid cells and path integration to robustly learn and recognize objects through movement and predict sensory stimuli after movement. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an object. In the entorhinal cortex, grid cells represent the location of an animal in its environment, and this location is updated through movement and path integration. The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. Numenta Inc., Redwood City, CA, United States.Marcus Lewis * Scott Purdy Subutai Ahmad Jeff Hawkins
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