  Extracting stimulus features from neuronal ensembles is of great interest to
the development of neuroprosthetics that project sensory information directly
to the brain via electrical stimulation. Machine learning strategies that
optimize stimulation parameters as the subject learns to interpret the
artificial input could improve device efficacy, increase prosthetic
performance, ensure stability of evoked sensations, and improve power
consumption by eliminating extraneous input. Recent advances extending deep
learning techniques to non-Euclidean graph data provide a novel approach to
interpreting neuronal spiking activity. For this study, we apply graph
convolutional networks (GCNs) to infer the underlying functional relationship
between neurons that are involved in the processing of artificial sensory
information. Data was collected from a freely behaving rat using a four
infrared (IR) sensor, ICMS-based neuroprosthesis to localize IR light sources.
We use GCNs to predict the stimulation frequency across four stimulating
channels in the prosthesis, which encode relative distance and directional
information to an IR-emitting reward port. Our GCN model is able to achieve a
peak performance of 73.5% on a modified ordinal regression performance metric
in a multiclass classification problem consisting of 7 classes, where chance is
14.3%. Additionally, the inferred adjacency matrix provides a adequate
representation of the underlying neural circuitry encoding the artificial
sensation.
