Abstract
The human nervous system is an ensemble of connected neuronal networks. Modeling and system identification of the human nervous system helps us understand how the brain processes sensory input and controls responses at the systems level. This study aims to propose an advanced approach based on a hierarchical neural network and non-linear system identification method to model neural activity in the nervous system in response to an external somatosensory input. The proposed approach incorporates basic concepts of Non-linear AutoRegressive Moving Average Model with eXogenous input (NARMAX) and neural network to acknowledge non-linear closed-loop neural interactions. Different from the commonly used polynomial NARMAX method, the proposed approach replaced the polynomial non-linear terms with a hierarchical neural network. The hierarchical neural network is built based on known neuroanatomical connections and corresponding transmission delays in neural pathways. The proposed method is applied to an experimental dataset, where cortical activities from ten young able-bodied individuals are extracted from electroencephalographic signals while applying mechanical perturbations to their wrist joint. The results yielded by the proposed method were compared with those obtained by the polynomial NARMAX and Volterra methods, evaluated by the variance accounted for (VAF). Both the proposed and polynomial NARMAX methods yielded much better modeling results than the Volterra model. Furthermore, the proposed method modeled cortical responded with a mean VAF of 69.35% for a three-step ahead prediction, which is significantly better than the VAF from a polynomial NARMAX model (mean VAF 47.09%). This study provides a novel approach for precise modeling of cortical responses to sensory input. The results indicate that the incorporation of knowledge of neuroanatomical connections in building a realistic model greatly improves the performance of system identification of the human nervous system.
Highlights
The human nervous system is an integrated, large-scale system consisting of connected neuronal networks
The residual error mainly appears in the high-frequency components, which is likely related to the fast dynamics of EEG oscillation as background noise in the system
This work proposes an advanced modeling approach based on the non-linear autoregressive moving average with exogenous inputs (NARMAX) framework and a hierarchical neural network to model cortical activity in response to flexion and extension stretch perturbations at the wrist
Summary
The human nervous system is an integrated, large-scale system consisting of connected neuronal networks. Efforts have been made through previous studies on building a mathematical model of the human nervous system from neurons and circuits to largescale neural networks. The non-linearity of the nervous system enables the rich, task-relevant neural encoding, and communication, while the fast dynamics (including both linear and non-linear components) allow for efficient processing and transmission of neural information (Friston, 2000). These two basic properties of the human nervous system, as well as the poor signal-to-noise of measured neural signals, significantly increase the difficulty of building a precise model to describe the behavior of the human nervous system
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.