  Spontaneous cortical activity -- the ongoing cortical activities in absence
of intentional sensory input -- is considered to play a vital role in many
aspects of both normal brain functions and mental dysfunctions. We present a
centered Gaussian-binary Deep Boltzmann Machine (GDBM) for modeling the
activity in early cortical visual areas and relate the random sampling in GDBMs
to the spontaneous cortical activity. After training the proposed model on
natural image patches, we show that the samples collected from the model's
probability distribution encompass similar activity patterns as found in the
spontaneous activity. Specifically, filters having the same orientation
preference tend to be active together during random sampling. Our work
demonstrates the centered GDBM is a meaningful model approach for basic
receptive field properties and the emergence of spontaneous activity patterns
in early cortical visual areas. Besides, we show empirically that centered
GDBMs do not suffer from the difficulties during training as GDBMs do and can
be properly trained without the layer-wise pretraining.
