NeuroVis: Real-Time Neural Information Measurement and Visualization of Embodied Neural Systems

Abstract

Understanding the real-time dynamical mechanisms of neural systems remains a significant issue, preventing the development of efficient neural technology and user trust. This is because the mechanisms, involving various neural spatial-temporal ingredients [i.e., neural structure (NS), neural dynamics (ND), neural plasticity (NP), and neural memory (NM)], are too complex to interpret and analyze altogether. While advanced tools have been developed using explainable artificial intelligence (XAI), node-link diagram, topography map, and other visualization techniques, they still fail to monitor and visualize all of these neural ingredients online. Accordingly, we propose here for the first time “NeuroVis,” real-time neural spatial-temporal information measurement and visualization, as a method/tool to measure temporal neural activities and their propagation throughout the network. By using this neural information along with the connection strength and plasticity, NeuroVis can visualize the NS, ND, NM, and NP via i) spatial 2D position and connection, ii) temporal color gradient, iii) connection thickness, and iv) temporal luminous intensity and change of connection thickness, respectively. This study presents three use cases of NeuroVis to evaluate its performance: i) function approximation using a modular neural network with recurrent and feedforward topologies together with supervised learning, ii) robot locomotion control and learning using the same modular network with reinforcement learning, and iii) robot locomotion control and adaptation using another larger-scale adaptive modular neural network. The use cases demonstrate how NeuroVis tracks and analyzes all neural ingredients of various (embodied) neural systems in real-time under the robot operating system (ROS) framework. To this end, it will offer the opportunity to better understand embodied dynamic neural information processes, boost efficient neural technology development, and enhance user trust.

Global research trends and hotspots of artificial intelligence research in spinal cord neural injury and restoration—a bibliometrics and visualization analysis

“…For example, image segmentation models based on convolutional neural networks can make excellent contributions to imaging parameters, disease classification, and diagnosis of spinal cord neural injury patients before and after surgery ( 36 , 37 ). Second, AI can track and analyze in real-time all neural components of various nervous systems, i.e., neural structure, neurodynamics, neuroplasticity, and neural memory ( 38 ).…”

Self‐Organized Stick Insect‐Like Locomotion under Decentralized Adaptive Neural Control: From Biological Investigation to Robot Simulation

“…Additionally, it could not store the emerged gaits for gait recovery. This is because it lacks neural communication between the leg control units, which is an important component for wave gait control and gait memorization, as demonstrated in our previous studies [ 87,101 ] and in neuroWalknet. [ 51 ] Thus, in the future, we will include x – y plane foot trajectories of stick insect data into our control system and further investigate the integration of leg posture control as well as local leg extension and elevation control or neuroWalknet intraleg control.…”

“…[ 51 ] Thus, in the future, we will include x – y plane foot trajectories of stick insect data into our control system and further investigate the integration of leg posture control as well as local leg extension and elevation control or neuroWalknet intraleg control. We will further enhance the control system by adding adaptive neural coupling mechanisms (i.e., neural communication) to store emerged gait patterns in the neural structure [ 87 ] and accomplish other stable gaits. [ 101 ] We will also implement muscle models [ 102 ] to achieve high adaptability for traveling over challenging terrain.…”

“…In principle, the neural control architecture with foot contact sensory feedback and adaptation can also function for self-organized locomotion and adaptation on irregular terrain with a certain level of roughness (see ref. [87] for an example of self-organized quadruped locomotion on irregular terrain). One can also extend the control to achieve curve walking by regulating the magnitude of the motor neuron output of each TC-joint through an additional scaling factor.…”