Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines

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 visual cortex 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 cortical activity of visual cortex. Specifically, filters having the same orientation preference tend to be active together during random sampling. Our work demonstrates the centered GDBM's potential for modeling cortical activity in early visual cortex. Besides, we show empirically that centered GDBMs do not suffer from difficulties during training as GDBMs do and can be properly trained without layer-wise pretraining.