On the Relationship Between LFP & Spiking Data
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.
Summary:The authors propose a convolutional dictionary model that captures relationships between spikes and LFP signals across multiple brain regions. Significant contributions are 1) the use of a clustering prior on the dictionary elements, 2) the ability to model dynamics of the dictionary elements, and 3) the corresponding mean-field variational inference procedure for the model.The authors then present a multitude of results on two large datasets. They find that time-varying dictionary elements yield superior performance over static elements, and that the use of clustering allows for improved interpretability of the data.In the novel environment experiment, they find that some dictionary elements change at the introduction to the new environment while others do not – and that the clustering reveals, for example, that a particular stable element is mostly associated with HP cells.Quality:The authors show convincingly that clustering and dictionary dynamics offer significant improvements both in quantitative fit and qualitative interpretability over simpler spiking-to-LFP models. A strong point of the paper is the variety of results presented on both datasets, showcasing the utility of the method.The model choices are reasonable. For example, the observation that similar results were found for a wide range of Bs suggest that the choice of modeling the dynamics over large discrete bins is sensible.With that said, what motivation is there for a first-order AR process over higher orders? This seems a stringent limitation on the dynamics of the dictionary elements.The paper might benefit from a discussion on how both spiking and LFP signals change over the course of both experiments. That is, are the dynamics of the dictionary elements due to changes in spiking statistics, LFP signals, or both? The emphasis was on the changes in dictionary elements themselves, illustrating that it is the relationship between spikes and LFP that changes over the course of the experiment, but it might help to understand this point in relation to the spiking and LFP signals themselves.Clarity:This paper is exceptionally clear.Small details:- There is a mismatch in the text on page 6 and the corresponding figures 2 and 3 (text says thalamus LFP, figure says HP LFP, etc)- best values not bolded in table 2.- how many clusters are typically identified. E.g. figure 2 shows 3 of how many?Originality:The method is original, and the authors present several original types of analyses one could perform with the method (e.g. see results in figure 3).Significance:This work appears to offer significant improvements over previous methods, and has potential to see widespread use as multi-region datasets become more common.