Abstract
Inferotemporal cortex (IT) in humans and other primates is topo-graphically organized, containing multiple hierarchically-organized areas selective for particular domains, such as faces and scenes. This organization is commonly viewed in terms of evolved domain-specific visual mechanisms. Here, we develop an alternative, domain-general and developmental account of IT cortical organization. The account is instantiated as an Interactive Topographic Network (ITN), a form of computational model in which a hierarchy of model IT areas, subject to connectivity-based constraints, learns high-level visual representations optimized for multiple domains. We find that minimizing a wiring cost on spatially organized feedforward and lateral connections within IT, combined with constraining the feedforward processing to be strictly excitatory, results in a hierarchical, topographic organization. This organization replicates a number of key properties of primate IT cortex, including the presence of domain-selective spatial clusters preferentially involved in the representation of faces, objects, and scenes, columnar responses across separate excitatory and inhibitory units, and generic spatial organization whereby the response correlation of pairs of units falls off with their distance. We thus argue that domain-selectivity is an emergent property of a visual system optimized to maximize behavioral performance while minimizing wiring costs.
Significance Statement We introduce the Interactive Topographic Network, a framework for modeling high-level vision, to demonstrate in computational simulations that the spatial clustering of domains in late stages of the primate visual system may arise from the demands of visual recognition under the constraints of minimal wiring costs and excitatory between-area neuronal communication. The learned organization of the model is highly specialized but not fully modular, capturing many of the properties of organization in primates. Our work is significant for cognitive neuroscience, by providing a domain-general developmental account of topo-graphic functional specialization, and for computational neuroscience, by demonstrating how well-known biological details can be successfully incorporated into neural network models in order to account for critical empirical findings.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
N.M.B., M.B., and D.C.P. conceived of the work. N.M.B. developed software and performed simulations and data analyses. M.B. and D.C.P supervised the project. N.M.B. wrote the first draft of the paper. N.M.B, M.B., and D.C.P. revised the paper.
The authors declare no competing interests.
