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A Unified Approach To The Nonlinearities Of Visual Neurons: The Curved Geometry Of Neural Response Surfaces

Author
Golden, James
Abstract
The responses of visual cortical neurons are highly nonlinear functions of image stimuli. I present a geometric view of these nonlinear responses and classify them as forms of selectivity or invariance, building on a body of established work. With the sparse coding network, a well-known network model of V1 computation, I attempt to quantify selectivity and invariance by measuring the curvature of neural response surfaces in both low-dimensional subspaces and image state space. I argue that this geometric view allows the precise quantification of feature selectivity and invariance in network models in a way that provides insight into the computations necessary for object recognition, and that this view may be a useful tool for future physiological experiments.
Date Issued
2015-08-17Subject
neuroscience; vision; neural network
Committee Chair
Field,David James
Committee Member
Cutting,James Eric; Edelman,Shimon J.; Finlay,Barbara L.
Degree Discipline
Psychology
Degree Name
Ph. D., Psychology
Degree Level
Doctor of Philosophy
Type
dissertation or thesis