Abstract
The neocortex is capable of anticipating the sensory results of movement but the neural mechanisms are poorly understood. 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. In this paper, we propose that sensory neocortex incorporates movement using grid cell-like neurons that represent the location of sensors on an 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. A layer of cells consisting of several grid cell-like modules represents a location in the reference frame of a specific object. 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. 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. Simulations show that the model can learn hundreds of objects even when object features alone are insufficient for disambiguation. 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. We propose that the subgranular layers of cortical columns employ grid cell-like mechanisms to represent object specific locations that are updated through movement.
Highlights
Our brains learn about the outside world by processing our sensory inputs and movements
We propose that the neocortex uses analogs of grid cells to model objects just as the hippocampal formation uses them to model environments
We propose that Layer 4 uses its input from Layer 6 to predict sensory input
Summary
Our brains learn about the outside world by processing our sensory inputs and movements. As we touch an object, survey a visual scene, or explore an environment, the brain receives a series of sensations and movements, a sensorimotor sequence. Cortical areas that are traditionally viewed as sensory areas are known to integrate the motor stream into their processing. We perceive a stable image of the world, usually oblivious to the fact that our eyes are making multiple saccadic movements per second. Many neurons in the visual cortex that represent a particular stimulus anticipate the stimulus before it lands in the cell’s receptive field (Duhamel et al, 1992). Responses in auditory cortex are predictively suppressed by motor signals (Schneider and Mooney, 2018). In somatosensation, when moving our fingers over familiar objects we quickly notice discrepancies suggesting we make tactile predictions that are specific to particular objects
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