Signal denoising through topographic modularity of neural circuits

Information from the sensory periphery is conveyed to the cortex via structured projection pathways that spatially segregate stimulus features, providing a robust and efficient encoding strategy. Beyond sensory encoding, this prominent anatomical feature extends throughout the neocortex. However, the extent to which it influences cortical processing is unclear. In this study, we combine cortical circuit modeling with network theory to demonstrate that the sharpness of topographic projections acts as a bifurcation parameter, controlling the macroscopic dynamics and representational precision across a modular network. By shifting the balance of excitation and inhibition, topographic modularity gradually increases task performance and improves the signal-to-noise ratio across the system. We show that this is a robust and generic structural feature that enables a broad range of behaviorally-relevant operating regimes, and provide an in-depth theoretical analysis unravelling the dynamical principles underlying the mechanism.

Equivalent Circuit Modeling and Analysis of Aerodynamic Vortex-induced Piezoelectric Energy Harvesting

Low-speed wind energy has potential to be captured for powering micro-electro-mechanical systems or sensors in remote inaccessible place by piezoelectric energy harvesting from vortex-induced vibration (VIV). Conventional theory or finite-element analysis mostly considers a simple pure resistance as interface circuit because of the complex fluid-solid-electricity coupling in aeroelastic piezoelectric energy harvesting. However, the output alternating voltage should be rectified to direct voltage to be used in practical occasions, where the theoretical analysis and finite-element analysis for complex interface may be cumbersome or difficult. To solve this problem, this paper presents an equivalent circuit modeling (ECM) method to analyze the performance of vortex-induced energy harvesters. Firstly, the equivalent analogies from the mechanical and fluid domain to the electrical domain are built. The linear mechanical and fluid elements are represented by standard electrical elements. The nonlinear elements are represented by electrical non-standard user-defined components. Secondly, the total fluid-solid-electricity coupled mathematical equations of the harvesting system are transformed into electrical formulations based on the equivalent analogies. Finally, the entire ECM is established in a circuit simulation software to perform system-level transient analyses. The simulation results from ECM have good agreement with the experimental measurements. Further parametric studies are carried out to assess the influences of wind speed and resistance on the output power of the alternating circuit interface and the capacitor filter circuit. At wind speed of 1.2 m/s, the energy harvester could generate an output power of 81.71 μW with the capacitor filter circuit and 114.64 μW with the alternating circuit interface. The filter capacitance is further studied to ascertain its effects on the stability of output and the settling time.

Measurement and interpretation of electrical signals in transient electrical discharges

Electrical discharges in experiments like Exploding Wire, Plasma Focus, or Z-pinch, involve regions where strong transient electrical currents generates magnetic flux variations within the limits of experiment and diagnostics regions. Due to different experimental conditions, time duration of the transient phase may vary from negligible to play an important role in the explanation of the measured signal of the experiment, in which case Faraday's law of induction cannot be neglected when analyzing the electrical signals. In this work the effects of circuit modeling taking into account Faraday's law will be discussed for the exploding wire experiment in a more detailed way than previous works.

Time-Domain Circuit Modelling for Hybrid Supercapacitors

Classic circuit modeling for supercapacitors is limited in representing the strongly non-linear behavior of the hybrid supercapacitor technology. In this work, two novel modeling techniques suitable to represent the time-domain electrical behavior of a hybrid supercapacitor are presented. The first technique enhances a well-affirmed circuit model by introducing specific non-linearities. The second technique models the device through a black-box approach with a neural network. Both the modeling techniques are validated experimentally using a workbench to acquire data from a real hybrid supercapacitor. The proposed models, suitable for different supercapacitor technologies, achieve higher accuracy and generalization capabilities compared to those already presented in the literature. Both modeling techniques allow for an accurate representation of both short-time domain and steady-state simulations, providing a valuable asset in electrical designs featuring supercapacitors.