Introduction

Understanding how the brain performs object categorization is of significant interest for cognitive neuroscience as well as for machine vision. In the past decade, building on earlier efforts (Fukushima 1980; Perrett and Oram 1993; Wallis and Rolls 1997), there has been significant progress in understanding the neural mechanisms underlying object recognition in the brain (Kourtzi and DiCarlo 2006; Peissig and Tarr 2007; Riesenhuber and Poggio 1999b; Riesenhuber and Poggio 2000; Riesenhuber and Poggio 2002; Serre et al. 2007a). There is now a quantitative computational model of the ventral visual pathway in primates that models rapid object recognition (Riesenhuber and Poggio 1999b; Serre et al. 2007a), putatively based on a single feedforward pass through the visual system (Thorpe and Fabre-Thorpe 2001). The model has been validated through a number of experiments that have confirmed nontrivial qualitative and quantitative predictions of the model (Freedman et al. 2003; Gawne and Martin 2002; Jiang et al. 2007; Jiang et al. 2006; Lampl et al. 2004). In the domain of machine vision, the success of the biological model in accounting for human object recognition performance (see, e.g., (Serre et al. 2007b)) has led to the development of a family of biologically inspired machine vision systems (see, e.g., (Marin-Jimenez and Perez de la Blanca, 2006; Meyers and Wolf, 2008; Mutch and Lowe, 2006; Serre et al. 2007c)).