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Front. Syst. Neurosci. | doi: 10.3389/fnsys.2019.00013

Deep Neural Networks for Modeling Neural Spiking in S1 Cortex

  • 1Department of Physiology Feinberg School of Medicine, Northwestern University, United States

Somatosensation is composed of two distinct modalities: touch, arising from sensors in the skin, and proprioception, resulting primarily from sensors in the muscles, combined with these same cutaneous sensors. In contrast to the wealth of information about touch, we know surprisingly little about the nature of the signals giving rise to proprioception at the cortical level. Here we investigate the use of Artificial Neural Networks (ANNs) to model the relationship between the firing rates of single neurons in the somatosensory cortex (S1) and several types of kinematic variables related to arm movement. To gain a better understanding of how these kinematic variables interact to create the proprioceptive responses recorded in our datasets, we train ANNs under different conditions, each involving a different set of input and output variables. We find that the addition of information about joint angles and/or muscle lengths significantly improves the prediction of neural firing rates. Our results thus provide new insight regarding the complex representations of the limb motion in S1. In addition, we conduct numerical experiments to determine the sensitivity of ANN models to various choices of training design and hyper-parameters. Our results provide a baseline and new tools for future research that utilizes machine learning to better describe and understand the activity of neurons in S1.

Keywords: Somatosensory Cortex, limb-state encoding, single neurons, reaching, monkey, artificial neural networks

Received: 04 Aug 2018; Accepted: 11 Mar 2019.

Edited by:

Maria V. Sanchez-Vives, August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Spain

Reviewed by:

Dong Xu, University of Missouri, United States
Mikhail Lebedev, Duke University, United States  

Copyright: © 2019 Lucas, Tomlinson, Rohani, Chowdhury, Solla, Katsaggelos and Miller. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Lee E. Miller, Northwestern University, Department of Physiology Feinberg School of Medicine, Evanston, 60611, United States, LM@Northwestern.EDU