Recognizing retinal ganglion cells in the dark
Many neural circuits are composed of numerous distinct cell types that perform different operations on their inputs, and send their outputs to distinct targets. Therefore, a key step in understanding neural systems is to reliably distinguish cell types. An important example is the retina, for which present-day techniques for identifying cell types are accurate, but very labor-intensive. Here, we develop automated classifiers for functional identification of retinal ganglion cells, the output neurons of the retina, based solely on recorded voltage patterns on a large scale array. We use per-cell classifiers based on features extracted from electrophysiological images (spatiotemporal voltage waveforms) and interspike intervals (autocorrelations). These classifiers achieve high performance in distinguishing between the major ganglion cell classes of the primate retina, but fail in achieving the same accuracy in predicting cell polarities (ON vs. OFF). We then show how to use indicators of functional coupling within populations of ganglion cells (cross-correlation) to infer cell polarities with a matrix completion algorithm. This can result in accurate, fully automated methods for cell type classification.
The authors propose to identify different types of retinal ganglion cells based on their electrical image in high-density MEA recordings, their inter-spike interval distribution and their couplings to other cells. To this end, the authors focus on the problem of identifying suitable features from these measurements to then train logistic regression or other classifiers for inferring the cell type. In addition, they use matrix completion techniques for inferring the cell polarity (ON vs OFF). Identifying retinal ganglion cell types is clearly a timely topic (reviewed recently by Sanes and Masland, Annual Reviews of Neuroscience, 2015) and the approach chosen by the authors is creative and interesting: typical techniques either focus on anatomical reconstructions or functional responses. However, I found the the paper was not well structured, so that it was difficult to assess precisely what was done and what are the novel contributions. The first contribution was to use certain features and apply a logistic regression (or other) classifier. The features used for the classification algorithm in lines 79-101 are not well described. The authors should describe the manually chosen filter banks in the main paper, not only define them in the appendix. They should also define what slices are and how they look, how the filter banks were chosen and what the clusters were. It seems to me that much of the information later presented in section 4 should have already been worked into section 2. The second contribution is to estimate a "polarity" vector by exploiting the cross-correlation matrix and the fact that types of same polarity have positive correlations. It is a bit unclear how this task relates to the previous one. They describe their approach to this "matrix completion" problem in some detail on page 3, which again is very dense and full of jargon (e.g. what is a lifted matrix?). Theorem 1 seems oddly out of place - while this may be an interesting theoretical result, a data analysis paper seems to me not the place to advertise it. Section 5.2 seems of place after the results and I am not sure I understand its relevance (partially because Fig. 4 is hard to read and lacks meaningful labels). The evaluation of the methods seems mostly performed withcare and using interesting setups, e.g. the transfer between datasets. Unfortunately, the hand labeled data only includes the standard set of ON/OFF midget and parasol RGCs, so the evaluation is restricted to those types. In some sense, those are the easy ones, as they are so abundant in the data and can be clearly identified in primate recordings. Of course, for testing a new method that may be fine. The data augementation for building in invariances is a nice trick, albeit not new. The results seem are ok, but it is hard to tell what a good baseline would be to compare against - on the task of polarity identification, the method devised by the authors only performs a little better than just exploiting ISI/CCs. Indeed, it is unclear what the values in () in Table 1 are, so it may be that the author's method does not significantly outperforms the simpler methods. In line 351ff, the authors write that performance was >90% on 3 datasets for polarity inference, and ~66% for the rest. Are those included in the numbers reported in the table and the figures? What is the difference between the datasets? Then they say they only show the results for 4/10 datasets? Which ones? Regarding type identification, features from the electrical image clearly improve it, although random features seem to be as good as handcrafted ones (~93% correct). What about PCA features of the electrical image (or other standard feature sets one might think of)? In Fig 2 right, I am somewhat puzzled by the red curve. Wouldn't you expect that this should be high where green and blue curves are high? Can the authors provide any insight into why the red curve is only high for smaller distances, although the task is to infer polarity as for the green curve? In Fig. 3, error bars are again unclear. An analysis of the false positive/false negative rates would have been nice to see.