A dynamical model for generating synthetic data to quantify active tactile sensing behavior in the rat

As it becomes possible to simulate increasingly complex neural networks, it becomes correspondingly important to model the sensory information that animals actively acquire: the biomechanics of sensory acquisition directly determines the sensory input and therefore neural processing. Here, we exploit the tractable mechanics of the well-studied rodent vibrissal (“whisker”) system to present a model that can simulate the signals acquired by a full sensor array actively sampling the environment. Rodents actively “whisk” ∼60 vibrissae (whiskers) to obtain tactile information, and this system is therefore ideal to study closed-loop sensorimotor processing. The simulation framework presented here, WHISKiT Physics, incorporates realistic morphology of the rat whisker array to predict the time-varying mechanical signals generated at each whisker base during sensory acquisition. Single-whisker dynamics were optimized based on experimental data and then validated against free tip oscillations and dynamic responses to collisions. The model is then extrapolated to include all whiskers in the array, incorporating each whisker’s individual geometry. Simulation examples in laboratory and natural environments demonstrate that WHISKiT Physics can predict input signals during various behaviors, currently impossible in the biological animal. In one exemplary use of the model, the results suggest that active whisking increases in-plane whisker bending compared to passive stimulation and that principal component analysis can reveal the relative contributions of whisker identity and mechanics at each whisker base to the vibrissotactile response. These results highlight how interactions between array morphology and individual whisker geometry and dynamics shape the signals that the brain must process.

Concept design and dynamics analysis of a novel lightweight vehicle suspension combined with driving units

A lightweighted suspension concept with integrated driving units into the longitudinal arm is proposed, to meet the increasing requirements from environments on both lightweight and propulsion to electric vehicles. This paper focuses on the structure concept design and ride dynamic analysis of the suspension with combined driving units. Besides conventional springs and shock absorbers, this concept suspension consists of a mass reduced axle structure, longitudinal arms, and electric driving units. The electric driving unit of the concept suspension arm is introduced by structural illustration first which in structure integrates the function as the suspension longitudinal arm and the function of electric propulsion to the vehicle. Meanwhile, a light brace structure with tube profiles is developed on the basis of topological optimization. Through the structure optimization, it can fulfill the suspension kinematic and compliance as well as mechanical requirements. The vehicle suspension realizes mass reduction not only from integration of driving units and suspension arm but also from structure optimization. In order to investigate the ride dynamics of the conceptual suspension, an analytical model for vehicle rear axle with a double lane road signal in accordance with International Organization for Standardization road surface profile is derived, with consideration of the integrated electric motor and linkage geometry. Simulation results are obtained to illustrate the ride dynamics in contrast to a conventional suspension benchmark. The simulation results indicate that the concept suspension has comparable ride dynamics performance as the reference suspension. Finally, the influences of the important parameters on ride dynamics are analyzed.

Information geometry, simulation and complexity in Gaussian random fields

Random fields are useful mathematical objects in the characterization of non-deterministic complex systems. A fundamental issue in the evolution of dynamical systems is how intrinsic properties of such structures change in time. In this paper, we propose to quantify how changes in the spatial dependence structure affect the Riemannian metric tensor that equips the model's parametric space. Defining Fisher curves, we measure the variations in each component of the metric tensor when visiting different entropic states of the system. Simulations show that the geometric deformations induced by the metric tensor in case of a decrease in the inverse temperature are not reversible for an increase of the same amount, provided there is significant variation in the system's entropy: the process of taking a system from a lower entropy state A to a higher entropy state B and then bringing it back to A, induces a natural intrinsic one-way direction of evolution. In this context, Fisher curves resemble mathematical models of hysteresis in which the natural orientation is pointed by an arrow of time.

Optimal Distance between Mobile Buoy and Target for Moving Long Baseline Positioning System

This paper investigates the problem of how to design the distance between a mobile buoy and the target to derive maximum positioning accuracy with a Moving Long Baseline (MLBL). To that end, the positioning model and the error sources of MLBL are derived, respectively. It is assumed that the position measurement of the mobile buoy and the distance measurement between the mobile buoy and the target are corrupted by white Gaussian noises, and the variance of the distance measurement is distance-dependent. Using tools from estimation theory, the Positioning Accuracy Metric (PAM) is designed with the distance error and the position errors are considered. Based on the PAM, the optimal distance between the mobile buoy and target is deduced when the mobile buoys are in optimal geometry. Simulation examples illustrate the results.