Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
In large part, rodents "see" the world through their whiskers, a powerful tactile sense enabled by a series of brain areas that form the whisker-trigeminal system. Raw sensory data arrives in the form of mechanical input to the exquisitely sensitive, actively-controllable whisker array, and is processed through a sequence of neural circuits, eventually arriving in cortical regions that communicate with decisionmaking and memory areas. Although a long history of experimental studies has characterized many aspects of these processing stages, the computational operations of the whisker-trigeminal system remain largely unknown. In the present work, we take a goal-driven deep neural network (DNN) approach to modeling these computations. First, we construct a biophysically-realistic model of the rat whisker array. We then generate a large dataset of whisker sweeps across a wide variety of 3D objects in highly-varying poses, angles, and speeds. Next, we train DNNs from several distinct architectural families to solve a shape recognition task in this dataset. Each architectural family represents a structurally-distinct hypothesis for processing in the whisker-trigeminal system, corresponding to different ways in which spatial and temporal information can be integrated. We find that most networks perform poorly on the challenging shape recognition task, but that specific architectures from several families can achieve reasonable performance levels. Finally, we show that Representational Dissimilarity Matrices (RDMs), a tool for comparing population codes between neural systems, can separate these higherperforming networks with data of a type that could plausibly be collected in a neurophysiological or imaging experiment. Our results are a proof-of-concept that DNN models of the whisker-trigeminal system are potentially within reach.
* Summary
  The authors of the paper "Toward Goal-Driven Neural Network Models
  for the Rodent Whisker-Trigeminal System" train networks for object
  detection on simulated whisker data. They compare several
  architectures of DNNs and RNNs on that task and report on elements
  that are crucial to get good performance. Finally they use a
  representational dissimilarity matrix analysis to distinguish
  different high performing network architectures, reasoning that this
  kind of analysis could also be used on neural data. 
* Comments
  What I like most about the paper is direction that it is
  taking. Visual datasets and deep neural networks have shown that
  there are interesting links between artificial networks and
  neuroscience that are worthwhile to explore. Carrying these
  approaches to other modalities is a laudable effort, in particular
  as interesting benchmarks often have advanced the field
  (e.g. imagenet). The paper is generally well written, and the main
  concepts are intuitively clear. More details are provided in the
  supplementary material, and the authors promise to share the code
  and the dataset with the community. 
  I cannot judge the physics behind simulating whiskers, but the
  authors seem to have taken great care to come up with a realistic
  and simulation, and verify that the data actually contains
  information about the stimulus. I think a bit more nuisance
  variables like slight airflow, as well as active whisking bouts
  would definitely make the dataset even more interesting, but I can
  see why it is a good idea to start with the "static" case first. 
  With regard to benchmarking, the authors made an effort to evaluate
  a variety of different networks and find interesting insights into
  which model components seem to matter. I would have wished for more
  details on how the networks are setup and trained. For example, how
  many hyperparameters were tried, what was the training schedule, was
  any regularization used, etc? The code will certainly contain these
  details, but a summary would have been good.
  The part about the representation dissimilarity matrix analysis is a
  bit short and it is not clear to me what can be learned from it. I
  guess the authors want to demonstrate that there are detectable
  differences in this analysis that could be used to compare neural
  performance to those networks. The real test for this analysis is
  neural data, of course, which the paper does not cover. 
  In general, I see the main contribution of the paper to be the
  dataset and the benchmarking analysis on it. Both together are a
  useful contribution to advance the field of neuroscience and machine
  learning.