Unlocking neural population non-stationarity using a hierarchical dynamics model
Neural population activity often exhibits rich variability. This variability can arise from single-neuron stochasticity, neural dynamics on short time-scales, as well as from modulations of neural firing properties on long time-scales, often referred to as neural non-stationarity. To better understand the nature of co-variability in neural circuits and their impact on cortical information processing, we introduce a hierarchical dynamics model that is able to capture both slow inter-trial modulations in firing rates as well as neural population dynamics. We derive a Bayesian Laplace propagation algorithm for joint inference of parameters and population states. On neural population recordings from primary visual cortex, we demonstrate that our model provides a better account of the structure of neural firing than stationary dynamics models.
This paper extends Poisson linear dynamical systems (PLDS) to account for the non-stationarity in neural spike trains.Their method (NPLDS) uses a hierarchical framework to find the latent variables for each trial, and also scale those latent variables multiplicatively for each trial.The latent variables are found with a linear dynamical system, and the inter-trial modulators are enforced to be smooth across trials with a Gaussian process.To fit the model, the authors devised the "Bayesian Laplacian" propagation and used an iterative procedure, which may be of interest to those outside the neuroscience field.The results are shown to be more predictive than the previous PLDS method, which suggests the added complexity helps performance.The results are still preliminary in some sense, as it is unclear how this method can be used to further understand population activity, but this method shows promise for other modelers.Quality:I found all aspects of the paper to have high quality (in terms of the work).I trust the results found, although I cannot confirm without testing the algorithm. Clarity:All text was well motivated and clear to follow.Please see below for small typos, etc. Originality:While the originality of the idea is not new, the method implementation is, and I believe of interest to other modelers. Significance:This paper is a nice step up from methods from the last NIPS 2014 conference, in which inter-trial variability was not considered.I believe this paper will be of interest to the modeling community, as the method is an interesting one to fit, and is a natural accept to NIPS.However, I question if this method will be of interest to the neuroscience community (specifically, how much the method will actually be used).The authors (and collaborators) will need to demonstrate the method is useful and returns interpretable results, as the method is too complicated to warrant the risk of spending weeks/months analyzing data with it.One interesting experiment would be to vary the level of anesthesia across trials, and see if this method's latents finds interpretable fluctuations.There is also no mention that the code will be released to the community; while this is not necessary for publication in NIPS, this would be in the authors' best interests.Personally, I would not use this method for two reasons: 1) the method has too many levels of complexity with various assumptions, which makes it tough to interpret the latents and 2) I would hesitate to play around with this model (i.e., spending a day throwing my data at it), as there are too many "knobs" to play with.Thus, I hope this method can make it into a journal paper with scientific results, and I would be much more excited about it! Notes while reading:- the intro was beautifully written; the logic was clear and well-founded 083 - instead of p neurons, perhaps you could use n to match other papers (subjective) 085 - let r = N, to follow other papers (subjective) 090 - I believe this equation can be written more concisely 093 - "d controls the mean firing rate of each cell" you need to make it clear this is across all trials (since later you say the mean firing rate changes across trials, and it's confusing 094 - "latent factors" do you mean x_t here, h(i), or both? 100 - give an example of u_t here.I also question the use of u_t...what if you would like to work with natural images, etc?If you are using cos, sin, and a binary variable, do you assume all your neurons are "simple"? Figure 1: you need to say the same stimulus is shown for each trial; do you ever reference figure 1 in the text?also I would suggesting changing "recording" to "trial"...recording is not used in the text 124 - "mean firing rates vary across trials" I tripped here, because I remembered d should be a fixed mean firing rate; see if you can clarify this more by explicitly saying h(i) 205 - "where used the vectorized" typo 214 - "obtaining the the approximate" typo 255 - it would be nice to define z earlier before referencing Figure 2, it took some time to figure out Fig 2 E...can you also think of the x-axis in terms of the number of trials? 298 - "units not used in the original study" --- why were they included here? 299 - say 4 s trials 300 - I find one problem with this model is that by using an LDS, you incur a "stimulus" signal which the experimenter must define.This limits the ability of the method to be applied to all types of data (e.g., natural images). 323 - you show RMSE here, why not use log-likelihood? is that approximate or tough to compute? Figure 4 - label both axes for every panel