The central focus of our Center is to develop a framework for studying the neural computations underlying object recognition and visual attention in visual cortex. The Center's framework is based on the collaboration of labs working on monkey physiology, cat physiology and human psychophysics with a quantitative computational theory providing the main conduit through which experimental results in one lab affect experiments in another lab. The model that flows from the theory provides a novel way to drive a collaborative enterprise, providing a way to integrate the data, to check their consistency, to suggest new experiments and to interpret the results. The theory itself, based on two existing models for recognition and attentional saliency, will not only guide the experiments and drive synergies between different labs but will also evolve as an effect of the experimental results. The research is organized into three main projects, defined by geographical location and scientific questions, rather than discipline. In the MIT project, the labs of Tomaso Poggio, Earl Miller and James DiCarlo will be guided by a quantitative hierarchical model of recognition, probing the relations between identification and categorization and the properties of selectivity and invariance of recognition, especially with natural image clutter, in IT and PFC cortex of behaving macaque monkeys. In the Northwestern project, the lab of David Ferster will test a key prediction of the model about the nature of the pooling operation (a max operation vs. a linear sum) performed by complex cells in area 17, using very similar stimuli affected by clutter. The experiments will be done in the anesthetized cat, intracellularly, to allow for a characterization of the underlying circuit and biophysical mechanisms. In the Caltech project, the lab of Christof Koch will collaborate with Ferster lab on biophysical simulations of Vl circuits. It will also test -- using human psychophysics with stimuli configurations similar to the ones used by Jim DiCarlo -- the conditions under which attention is needed in recognition of natural objects and scenes; from the data, in collaboration with Tomaso Poggio, Koch's lab will extend the basic model of recognition by integrating it with the existing model of bottom-up saliency. A unique aspect of our Center is that, the computational component, centered around a quantitative theory of recognition, is the generic tool that drives interactions between the investigators, in addition to the standard pairwise interactions: the model suggests experiments and guides their planning and interpretation; the experimental results from one lab impact, through the model, work done in another lab, including model development, as well as their interpretation and what to do next. Ultimately, the whole process should lead to a better and more coherent understanding of the neural mechanisms of visual recognition.