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Abstract:
A key question in neuroscience is how to encode sensory
stimuli such as images and sounds. Motivated by studies of
response properties of neurons in the early cortical areas, we
propose an encoding scheme that dispenses with absolute measures
of signal intensity or contrast and uses, instead, only local
ordinal measures. In this scheme, the structure of a signal is
represented by a set of equalities and inequalities across
adjacent regions. In this paper, we focus on characterizing the
fidelity of this representation strategy. We develop a
regularization approach for image reconstruction from ordinal
measures and thereby demonstrate that the ordinal representation
scheme can faithfully encode signal structure. We also present a
neurally plausible implementation of this computation that uses
only local update rules. The results highlight the robustness and
generalization ability of local ordinal encodings for the task of
pattern classification.
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