† Randall C. O'Reilly and Dean Wyatte have contributed equally to this work.How does the brain learn to recognize objects visually, and perform this difficult feat robustly in the face of many sources of ambiguity and variability? We present a computational model based on the biology of the relevant visual pathways that learns to reliably recognize 100 different object categories in the face of naturally occurring variability in location, rotation, size, and lighting. The model exhibits robustness to highly ambiguous, partially occluded inputs. Both the unified, biologically plausible learning mechanism and the robustness to occlusion derive from the role that recurrent connectivity and recurrent processing mechanisms play in the model. Furthermore, this interaction of recurrent connectivity and learning predicts that high-level visual representations should be shaped by error signals from nearby, associated brain areas over the course of visual learning. Consistent with this prediction, we show how semantic knowledge about object categories changes the nature of their learned visual representations, as well as how this representational shift supports the mapping between perceptual and conceptual knowledge. Altogether, these findings support the potential importance of ongoing recurrent processing throughout the brain's visual system and suggest ways in which object recognition can be understood in terms of interactions within and between processes over time. Keywords: object recognition, computational model, recurrent processing, feedback, winners-take-all mechanism INTRODUCTIONOne of the most salient features of the mammalian neocortex is the structure of its connectivity, which provides for many forms of recurrent processing, where neurons mutually influence each other through direct, bidirectional interactions. There are extensive bidirectional excitatory and inhibitory connections within individual cortical areas, and almost invariably, every area that receives afferent synapses from another area, also sends back efferent synapses in return (Felleman and Van Essen, 1991;Scannell et al., 1995;Sporns and Zwi, 2004;Sporns et al., 2007). We describe an explicit computational model (LVis -Leabra Vision) of the function of this recurrent architecture in the context of visual object recognition, demonstrating a synergy between the learning and processing benefits of recurrent connectivity.Recurrent processing, for example, has been suggested to be critical for solving certain visual tasks such as figure-ground segmentation (Hupe et al., 1998;Roelfsema et al., 2002;Lamme and Roelfsema, 2000), which requires integration of information from outside the classical receptive field. We demonstrate how recurrent excitatory processing could provide a similar function in visual occlusion, which requires the organization of image fragments that span multiple receptive fields into a logical whole Gestalt and involves the filling-in of missing visual information Lerner et al., 2002;Rauschenberger et al., 2006;Weigelt et al., 2...

a b s t r a c t Emergent (http://grey.colorado.edu/emergent) is a powerful tool for the simulation of biologically plausible, complex neural systems that was released in August 2007. Inheriting decades of research and experience in network algorithms and modeling principles from its predecessors, PDP++ and PDP, Emergent has been redesigned as an efficient workspace for academic research and an engaging, easy-to-navigate environment for students. The system provides a modern and intuitive interface for programming and visualization centered around hierarchical, tree-based navigation and drag-anddrop reorganization. Emergent contains familiar, high-level simulation constructs such as Layers and Projections, a wide variety of algorithms, general-purpose data handling and analysis facilities and an integrated virtual environment for developing closed-loop cognitive agents. For students, the traditional role of a textbook has been enhanced by wikis embedded in every project that serve to explain, document, and help newcomers engage the interface and step through models using familiar hyperlinks. For advanced users, the software is easily extensible in all respects via runtime plugins, has a powerful shell with an integrated debugger, and a scripting language that is fully symmetric with the interface. Emergent strikes a balance between detailed, computationally expensive spiking neuron models and abstract, Bayesian or symbolic systems. This middle level of detail allows for the rapid development and successful execution of complex cognitive models while maintaining biological plausibility.

How does the brain bind together visual features that are processed concurrently by different neurons into a unified percept suitable for processes such as object recognition? Here, we describe how simple, commonly accepted principles of neural processing can interact over time to solve the brain’s binding problem. We focus on mechanisms of neural inhibition and top-down feedback. Specifically, we describe how inhibition creates competition among neural populations that code different features, effectively suppressing irrelevant information, and thus minimizing illusory conjunctions. Top-down feedback contributes to binding in a similar manner, but by reinforcing relevant features. Together, inhibition and top-down feedback contribute to a competitive environment that ensures only the most appropriate features are bound together. We demonstrate this overall proposal using a biologically realistic neural model of vision that processes features across a hierarchy of interconnected brain areas. Finally, we argue that temporal synchrony plays only a limited role in binding – it does not simultaneously bind multiple objects, but does aid in creating additional contrast between relevant and irrelevant features. Thus, our overall theory constitutes a solution to the binding problem that relies only on simple neural principles without any binding-specific processes.

Abstract. Monocular figure-ground segmentation is an important problem in the field of Artificial General Intelligence. A solution to this problem will unlock vast sets of training data, such as Google Images, in which salient objects of interest are situated against complex backgrounds. In order to gain traction on the figure-ground problem we enhanced the Leabra Vision (LVis) model, which is our state-of-the-art model of 3D invariant object recognition [8], such that it can continue to recognize objects against cluttered backgrounds that, while simple, are complex enough to substantially hurt object recognition performance. The principle of operation of the network is that it learns to use a low resolution view of the scene in which high spatial frequency information such as the background falls out of focus in order to predict which aspects of the high resolution scene are the figure. This filtered view then serves to enhance the figure in the input stages of LVis and substantially improves object recognition performance against cluttered backgrounds.