  We present a model of the primary visual cortex V1, guided by anatomical
experiments. Unlike most machine learning systems our goal is not to maximize
accuracy but to realize a system more aligned to biological systems. Our model
consists of the V1 layers 4, 2/3, and 5, with inter-layer connections between
them in accordance with the anatomy. We further include the orientation
selectivity of the V1 neurons and lateral influences in each layer. Our V1
model, when applied to the BSDS500 ground truth images (indicating LGN contour
detection before V1), can extract low-level features from the images and
perform a significant amount of distortion reduction. As a follow-up to our V1
model, we propose a V1-inspired self-organizing map algorithm (V1-SOM), where
the weight update of each neuron gets influenced by its neighbors. V1-SOM can
tolerate noisy inputs as well as noise in the weight updates better than SOM
and shows a similar level of performance when trained with high dimensional
data such as the MNIST dataset. Finally, when we applied V1 processing to the
MNIST dataset to extract low-level features and trained V1-SOM with the
modified MNIST dataset, the quantization error was significantly reduced. Our
results support the hypothesis that the ventral stream performs gradual
untangling of input spaces.
