Modeling correlations in spontaneous activity of visual cortex with centered Gaussian-binary deep Boltzmann machines
Authors: nan wang, Laurenz Wiskott, Dirk Jancke
ICLR 2014

Abstract: Spontaneous cortical activity -- the ongoing cortical activities in absence of sensory input -- are 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 visual cortex and relate the random sampling in DBMs to the spontaneous cortical activity. After training on natural image patches, the proposed model is able to learn the filters similar to the receptive fields of simple cells in V1. Furthermore, we show that the samples collected from random sampling in the centered GDBMs encompass similar activity patterns as found in the spontaneous cortical activity of the visual cortex. Specifically, filters having the same orientation preference tend to be active together during random sampling. Our work demonstrates the homeostasis learned by the centered GDBM and its potential for modeling visual cortical activity. Besides, the results support the hypothesis that the homeostatic mechanism exists in the cortex.


Peer review:
[Review]:
I have read the authors' replies and skimmed the revised paper. The authors put some effort into improving and streamlining the paper (though the writing has issues, see below). Some main issues such as lack of significance remain.

Like the other reviewers, I am not convinced that sampling from the model freely, without conditioning the visibles to some value, captures the experimental setup of [1]. That this is necessary to reproduce the relevant aspects of spontaneous activity appears to speak against the capability of the authors' model to capture the situation. Normatively, I don't see why spontaneous activity under lack of input should resemble the prior/model distribution, without further assumptions, such as active homeostatic mechanisms. Note that in the work of Berkes et al. that I mentioned earlier, they get around this by positing an additional global contrast variable that effectively renders the feature variables unobserved in darkness (they only mention this important aspect in the supporting material!).

In this context:

'the authors reported to make the same findings during presenting a uniform gray screen to the cats as having the cats in a darkened room.'

..but presumably the GDBM would *not* reproduce the results conditioned on a gray image as input?

Finally, the revision of the paper has unfortunately introduced a lot of grammar issues as well as some typos, mostly wrong singular/plurals and missing/inappropriate articles (examples: 'expectated'; 'a brain learn to synthesize'; l23 of algorithm 1, y_model should be y_data). The paper should be proofread properly in case of its acceptance.