title: Extending the Stabilized Supralinear Network model for binocular image processing.
abstract: The visual cortex is both extensive and intricate. Computational models are needed to clarify the relationships between its local mechanisms and high-level functions. The Stabilized Supralinear Network (SSN) model was recently shown to account for many receptive field phenomena in V1, and also to predict subtle receptive field properties that were subsequently confirmed in vivo. In this study, we performed a preliminary exploration of whether the SSN is suitable for incorporation into large, functional models of the visual cortex, considering both its extensibility and computational tractability. First, whereas the SSN receives abstract orientation signals as input, we extended it to receive images (through a linear-nonlinear stage), and found that the extended version behaved similarly. Secondly, whereas the SSN had previously been studied in a monocular context, we found that it could also reproduce data on interocular transfer of surround suppression. Finally, we reformulated the SSN as a convolutional neural network, and found that it scaled well on parallel hardware. These results provide additional support for the plausibility of the SSN as a model of lateral interactions in V1, and suggest that the SSN is well suited as a component of complex vision models. Future work will use the SSN to explore relationships between local network interactions and sophisticated vision processes in large networks.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1; Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1. Electronic address: bselby@uwaterloo.ca.
authors.forename: Ben
authors.initials: B
authors.lastname: Selby
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1; Centre for Theoretical Neuroscience, University of Waterloo, 200 University Ave W., Waterloo, Ontario, Canada N2L 3G1. Electronic address: bptripp@uwaterloo.ca.
authors.forename: Bryan
authors.initials: B
authors.lastname: Tripp
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D009949:Orientation / Q000502:physiology; D010775:Photic Stimulation / Q000379:methods*; D015348:Vision, Binocular / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology
keywords: Balanced excitation/inhibition; Convolutional neural networks; Interocular transfer of suppression; Primary visual cortex; Stabilized supralinear network