d93ed5b6db83be78efb0d05ae420158e-Reviews.html

This paper proposes determinantal point processes as a method to model inhibitory interactions in spike train data. The authors present a maximum likelihood approach based on stochastic gradient descent. This is an interesting idea that could potentially be a powerful non-factorial spike train model. However, the presentation here seems a bit unfocused, and it's not obvious to what extent DPPs will actually improve model accuracy. Since gain and periodic terms can easily be built into GLMs, it wasn't obvious to me that framing the problem as a DPP was worth the trouble.