The Emergence of Semantic Topography in a Neurally-Inspired Computational Model

Representations in sensory cortices are organized topographically: auditory cortex is organized tonotopically, somatosensory cortex is organized somatotopically, and visual cortex is organized retinotopically. Substantial progress has been made in understanding how topography develops at a neurocomputational level, particularly in the early and middle stages of processing in the visual system. We extend this work to investigate how higher-level semantic representations could develop based on topographic input from sensory maps in the ventral visual pathway. The receptive fields of cells in these maps correspond to the loci of activity within a cortical topography rather than explicitly coded sensory features. Using this model, we show that meaningful semantic representations at increasing levels of abstraction naturally emerge as a result of exposure to a set of visual stimuli. For example, when presented with a set of simple visual features (color, texture, size, and shape) the model develops semantic representations that distinguish basic level categories (e.g., dogs, tables, cars), superordinate categories (e.g., animals, furniture, vehicles), and living versus nonliving things. This work therefore offers a computationally explicit hypothesis about how semantic representations could emerge in the brain. Our results suggest the possibility that high-order concept representations may be encoded topographically much the same way as low-order sensory representations, and that these representations may be learned based on the same principles of neural computation known to be operating in sensory cortex.