Computational architectures mediating object recognition under occlusion

Mouse V1 Ca response
Computational architectures mediating object recognition under occlusion

Feedforward circuits have been shown to be very powerful as models of vision.  However, these architectures are apparently incapable of dealing with many visual tasks that the human visual system finds simple, such as identifying occluded figures.  Data from the Allen Institute for Brain Science Cortical Activity Map (CAM) project may give us the opportunity to explore this question.  What is the functional connectivity of cells in mouse V1 during awake passive viewing of various stimuli (gratings, noise, natural scenes)?  How is this connectivity involved in the computations of the visual cortex?  In what ways does it underlie or mediate information processing?  Can we find evidence that such connectivity is important for visual tasks such as identifying occluded figures?  Can we build recurrent computational architectures guided by these models that will overcome the limitations of known feedforward architectures?  We will address these questions by fitting statistical models to the CAM data sets from which we will derive functional connectivity.  These models will inform the development and training of new computational architectures for object recognition.

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