In defense of brain-inspired cognitive models

The human brain excels at recognizing objects across modalities. In my research I seek to harness the neurocomputational principles that underlie these cognitive abilities by building brain-inspired cognitive models of attention and object recognition. My models have given insight into human cognition while also excelling at machine perception. Specifically, in this dissertation I describe four brain-inspired models. I first describe SUN, a saliency-based model of task-driven visual search. SUN uses independent component analysis (ICA) to learn image filters with properties that are qualitatively similar to simple cells in primary visual cortex, and it uses these filters to predict human eye movements. Then I describe NIMBLE, an approach to active object recognition using SUN and simulated eye movements. Subsequently, I describe Gnostic Fields, a brain-inspired model that attacks the universal stimulus recognition problem. Gnostic Fields achieve state-of-the-art performance on benchmark datasets for music, image, and odor classification. Lastly, I combine Gnostic Fields with a space-variant model of visual processing learned using ICA and assess the model's ability to classify objects and faces in image