Building a mechanistic model of the development and function of the primary visual cortex James A. Bednar Institute for Adaptive and Neural Computation, The University of Edinburgh, 10 Crichton St., EH8 9AB Edinburgh, UK article info Article history: Available online 16 February 2012 Keywords: Topographic map Development Computational model Orientation map Visual system Cortical microcircuit Cortical column abstract Researchers have used a very wide range of different experimental and theoretical approaches to help understand mammalian visual systems. These approaches tend to have quite different assumptions, strengths, and weaknesses. Computational models of the visual cortex, in particular, have typically imple- mented either a proposed circuit for part of the visual cortex of the adult, assuming a very specific wiring pattern based on findings from adults, or else attempted to explain the long-term development of a visual cortex region from an initially undifferentiated starting point. Previous models of adult V1 have been able to account for many of the measured properties of V1 neurons, while not explaining how these properties arise or why neurons have those properties in particular. Previous developmental models have been able to reproduce the overall organization of specific feature maps in V1, such as orientation maps, but are generally formulated at an abstract level that does not allow testing with real images or analysis of detailed neural properties relevant for visual function. In this review of results from a large set of new, integrative models developed from shared principles and a set of shared software components, I show how these models now represent a single, consistent explanation for a wide body of experimental evi- dence, and form a compact hypothesis for much of the development and behavior of neurons in the visual cortex. The models are the first developmental models with wiring consistent with V1, the first to have realistic behavior with respect to visual contrast, and the first to include all of the demonstrated visual feature dimensions. The goal is to have a comprehensive explanation for why V1 is wired as it is in the adult, and how that circuitry leads to the observed behavior of the neurons during visual tasks. Ó 2012 Published by Elsevier Ltd. 1. Introduction Understanding how we see remains an elusive goal, despite more than half a century of intensive work using a wide array of experimental and theoretical techniques. Because each of these techniques has different assumptions, strengths, and weaknesses, it can be difficult to establish clear principles and conclusive evi- dence. To make significant progress in this area, it is important to consider how the existing data can be synthesized into a coher- ent explanation for a wide variety of phenomena. Computational models of the primary visual cortex (V1) could provide a platform for achieving such a synthesis, integrating re- sults across levels to provide an overall explanation for the main body of results. However, existing models typically fall into one of two categories with different aims, neither of which achieves this goal: (1) narrowly constrained models of specific aspects of adult cortical circuitry or function, or (2) abstract models of large-scale visual area development, accounting for only a few of the response properties of individual neurons within these areas. Existing models of type (1) (e.g. Chance et al., 1999; Adelson and Bergen, 1985; Albrecht and Geisler, 1991) have been able to show how a variety of specialized circuits (often mutually incom- patible) can account for most of the major observed functional properties of V1 neurons, but do not attempt to show how a single, general-purpose circuit could explain most of them at the same time. Because each specific phenomenon can often be explained by many different specialized models, it can be difficult or impos- sible to distinguish between different explanations. Moreover, just showing an example of how the property can be implemented does little to explain why neurons are arranged in this way, and what the circuit might contribute to the process of vision. Similarly, many existing models of type (2) have been able to ac- count for the large-scale organization of V1, such as its arrangement into topographic orientation maps (reviewed in Swindale (1996), Goodhill (2007), and Erwin et al. (1995)). Yet because the develop- mental models are formulated at an abstract level, they address only a few of the observed properties of V1 neurons (such as their orien- tation or eye preference), and it is again difficult to decide between 0928-4257/$ - see front matter Ó 2012 Published by Elsevier Ltd. http://dx.doi.org/10.1016/j.jphysparis.2011.12.001 E-mail address: jbednar@inf.ed.ac.uk URLs: http://homepages.inf.ed.ac.uk/jbednar Journal of Physiology - Paris 106 (2012) 194–211 Contents lists available at SciVerse ScienceDirect Journal of Physiology - Paris journal homepage: www.elsevier.com/locate/jphysparis