  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 decision-making 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 higher-performing
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 goal-driven DNN networks of the whisker-trigeminal system are potentially
within reach.
