In this lecture, I will use our work in visual neural science to illustrate the potential that large-scale computational modeling presents to neural science today. Neural science is primarily an experimental science, with major advances following closely upon advances in experimental technology. Similarly, advances in computational technology over the past two decades have positioned computational scientists to contribute to the theoretical understanding of neuronal systems. For some time now, our group at NYU has been developing a large-scale computational representation of an input layer of the primary visual cortex (V1) of Macaque monkey – the “front end” of the monkey’s visual system. Neurons in V1 are “edge detectors” – detecting the orientation of edges within the visual scene. Very recent experiments on mouse V1 from the Scanziani and Tao labs have used opto-genetics techniques to observe for the first time “thalamus to cortical” excitation separately from “cortical to cortical” excitation. These and many other experimental studies have produced a vast amount of information about Mouse V1, which has many differences from Monkey V1. For example, while Monkey V1 is tiled by an ordered map of orientation preference, mouse V1 is tiled by a disordered “salt and pepper” map. In recent work, we have developed a large-scale computational model of mouse V1, and have studied its neuronal response. Our mouse model reproduces experimental observations, and allows us to analyze the mechanisms by which the model achieves the observed responses – with its disordered map of orientation preference.