Gabor-like Image Filtering using a Neural Microcircuit
This work addresses image processing tasks for applications in computer vision or neuroscience, but it appears incremental as it adapts existing neural and Hebbian methods to a specific filtering problem.
The authors tackled the problem of implementing Gabor-like image filtering by developing a neural microcircuit using Hebbian-adaptive learning, which extracts uncorrelated action potentials to approximate such filtering and other image processing functions.
In this letter, we present an implementation of a neural microcircuit for image processing employing Hebbian-adaptive learning. The neuronal circuit utilizes only excitatory synapses to correlate action potentials, extracting the uncorrelated ones, which contain significant image information. This circuit is capable of approximating Gabor-like image filtering and other image processing functions