Neural Information Processing Systems http://nips.cc/

We would like to thank all the reviewers for their thoughtful comments and their enthusiasm for our work. These results are consistent with those of Zoltowski et al. [2020], where they found Laplace EM compared Section 3. Segmenting the continuous latent states for each population (which is equivalent to imposing hard constraints On top of that, the "sticky" parameterization of discrete state transitions reveals which neural populations C. elegans offers an illustrative demonstration of the mp-srSLDS For example, we explore interactions between ganglia in Appendix C. Thanks again for spending the time to provide valuable feedback on our work.

One of the goals of neuroscience is to identify neural networks that correlate with important behaviors, environments, or genotypes. This work proposes a strategy for identifying neural networks characterized by time-and frequency-dependent connectivity patterns, using convolutional dictionary learning that links spike-train data to local field potentials (LFPs) across multiple areas of the brain. Analytical contributions are: ( i) modeling dynamic relationships between LFPs and spikes; ( ii) describing the relationships between spikes and LFPs, by analyzing the ability to predict LFP data from one region based on spiking information from across the brain; and ( iii) development of a clustering methodology that allows inference of similarities in neurons from multiple regions. Results are based on data sets in which spike and LFP data are recorded simultaneously from up to 16 brain regions in a mouse.

However, most human dynamic-behavior models focus only on the sequence of users' actions, abstracting the intervals between actions (i.e., inter-temporal information). Statistical time-series models, for instance, study the variation of values in the data over time; however, such models do not explicitly capture the interdependence between actions and their intervals. While some point-process models incorporate intervals, they use them to predict only a single or a few event types rather than to characterize diverse human actions enriched with temporal information from massive data (Zhao et al., 2015; Mei and Eisner, 2017). Therefore, in contrast with the sophisticated advancement of statistical behavior models, understanding human behavior from the perspective of inter-temporal context remains a difficult and often elusive goal. W e perform actions in many different contexts--from using smartphones to walking across campus. Studying these situations can help us understand what human actions are like. Even the same action can differ depending on when and where it happens. Time intervals between actions provide crucial contextual information, and much literature shows that they can reveal human cognitive states (Stanovich and W est, 2000; 1 arXiv:2206.09535v2