Sparse convolutional coding for neuronal assembly detection
Cell assemblies, originally proposed by Donald Hebb (1949), are subsets of neurons firing in a temporally coordinated way that gives rise to repeated motifs supposed to underly neural representations and information processing. Although Hebb's original proposal dates back many decades, the detection of assemblies and their role in coding is still an open and current research topic, partly because simultaneous recordings from large populations of neurons became feasible only relatively recently. Most current and easy-to-apply computational techniques focus on the identification of strictly synchronously spiking neurons. In this paper we propose a new algorithm, based on sparse convolutional coding, for detecting recurrent motifs of arbitrary structure up to a given length. Testing of our algorithm on synthetically generated datasets shows that it outperforms established methods and accurately identifies the temporal structure of embedded assemblies, even when these contain overlapping neurons or when strong background noise is present. Moreover, exploratory analysis of experimental datasets from hippocampal slices and cortical neuron cultures have provided promising results. * Both authors contributed equally. † Majority of this work was done while co-author was at 3 . ‡ Majority of this work was done while co-author was at 1 .
31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA.
The paper proposes a new method for the identification of spiking motifs in neuronal population data.  The authors’ approach is to use a sparse convolutional coding model that is learned using an alternation between LASSO and matching pursuit.  The authors address the problem of false positives and compare their algorithm to 3 competing methods in both simulation experiments and real neural data.
It appears that the basic model is not original, but that the regularization has been improved, leading to a method that performs well beyond the state of the art.
I believe that the method could be of some use to the neuroscience community as it may be powerful enough to determine if population codes fire in coordinated assemblies and if so, what is the nature of the assemblies?  This is a step towards addressing these long-standing questions in the neuroscience community.
Overall, the paper is clearly written, the method is reasonably conceived and described, and the simulation studies were sufficiently conceived for a paper of this length. Some of the methods for evaluating performance were unclear.  I am not totally certain how to assess Figure 4, for example since lines 169-175 where the “functional association” metric is described is too impoverished to determine what was actually done and what is being compared across detection thresholds.
I also am of the opinion that the authors are over-interpreting their data analysis results when they claim that the motif appearance is related to re-activation of former assemblies (do they mean replay?).
Also, it appears that few of the motif activations in Figure 7 include all of the cells that are a part of the motif.  This is concerning as it is not a part of the model description and makes it appear as though there is a poor model fit to the data.