  Learning features invariant to arbitrary transformations in the data is a
requirement for any recognition system, biological or artificial. It is now
widely accepted that simple cells in the primary visual cortex respond to
features while the complex cells respond to features invariant to different
transformations. We present a novel two-layered feedforward neural model that
learns features in the first layer by spatial spherical clustering and
invariance to transformations in the second layer by temporal spherical
clustering. Learning occurs in an online and unsupervised manner following the
Hebbian rule. When exposed to natural videos acquired by a camera mounted on a
cat's head, the first and second layer neurons in our model develop simple and
complex cell-like receptive field properties. The model can predict by learning
lateral connections among the first layer neurons. A topographic map to their
spatial features emerges by exponentially decaying the flow of activation with
distance from one neuron to another in the first layer that fire in close
temporal proximity, thereby minimizing the pooling length in an online manner
simultaneously with feature learning.
