Feature binding and robust vision in machines and primates

Decades of research have shed light on some of the computational elements that enable the extraordinary visual capabilities of primates, yet the brain's solution to the binding problem remains unresolved. In particular, how are the relations of separately encoded features represented in a meaningful way? More recently, machine vision has experienced significant advances with the proliferation of deep-learning techniques, although robust object recognition comparable to primate vision remains out of reach. We propose that these two unresolved goals may be related, leveraging recent theoretical work in computational neuroscience to improve the robustness of machine vision systems. Similarly, we adopt concepts from robust computer vision systems and high-level computational neuroscience models to explore a possible biological implementation of binding at the neural level. We begin by implementing a network where grid cells are employed as a basis for encoding the spatial relations of features in vision, enabling rapid feature binding for novel sensory information. The utility of this system is then demonstrated on a series of challenging machine vision tasks, including object recognition under saccade-like visual inputs, demonstrating superior performance to traditional machine learning approaches. Further analyses explore the importance of spatial information for the network, and secondary benefits of the architecture such as robustness under continual learning, and the availability of predictive representations. Our results emphasize that this form of binding can serve as a basis for designing enhanced machine vision systems, and may represent computations in the primate brain that account for our current superiority over artificial systems on a variety of visual tasks. Subsequently, machine vision systems designed for robustness, along with a high-level computational neuroscience model, form the basis for exploring how biological neurons might implement learned feature binding to support the recognition of objects. In particular, we establish the biologically and computationally constrained settings under which a combination of axonal conduction delays, coincidence detection, and Spike-Timing-Dependent Plasticity (STDP) may be sufficient to enable the emergence of object-selective neurons that are both translation invariant and form-specific in their responses. Our combined analysis and simulation results suggest that such a computation represents a feasible, albeit potentially specialized, operation that the primate brain could leverage for robust object recognition. Finally, we explore the phenomenon of adversarial examples, suggesting that these may partly be a consequence of the low-dimensional object representations typically assumed in machine vision systems. Leveraging recent developments in theoretical neuroscience, we present a novel deep-learning architecture which approximates the notion of hierarchical binding. Augmented with representations designed to better capture the rich variation of natural objects, the architecture is shown to cause a variety of adversarial attack methods to fail. Additional analyses provide evidence that these results are related to the introduced hierarchical binding, and its influence on the geometry of the network's representations, providing a novel approach for the design of more robust machine vision systems.