Neurons integrate excitatory and inhibitory signals to produce their outputs, but the role of input timing in this integration remains poorly understood. Motion detection is a paradigmatic example of this integration, since theories of motion detection rely on different delays in visual signals. These delays allow circuits to compare scenes at different times to calculate the direction and speed of motion. Different motion detection circuits have different velocity sensitivity, but it remains untested how the response dynamics of individual cell types drive this tuning. Here, we sped up or slowed down specific neuron types in Drosophila's motion detection circuit by manipulating ion channel expression. Altering the dynamics of individual neuron types upstream of motion detectors increased their sensitivity to fast or slow visual motion, exposing distinct roles for excitatory and inhibitory dynamics in tuning directional signals, including a role for the amacrine cell CT1. A circuit model constrained by functional data and anatomy qualitatively reproduced the observed tuning changes. Overall, these results reveal how excitatory and inhibitory dynamics together tune a canonical circuit computation.
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http://dx.doi.org/10.1016/j.cub.2022.06.075 | DOI Listing |
ACS Appl Mater Interfaces
December 2024
College of Chemistry and Chemical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Polyurethane sponge is frequently selected as a substrate material for constructing flexible compressible sensors due to its excellent resilience and compressibility. However, being highly hydrophilic and flammable, it not only narrows the range of use of the sensor but also poses a great potential threat to human safety. In this paper, a conductive flexible piezoresistive sensor (CHAP-PU) with superhydrophobicity and high flame retardancy was prepared by a simple dip-coating method using A-CNTs/HGM/ADP coatings deposited on the surface of a sponge skeleton and modified with polydimethylsiloxane.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Radiology, Seoul National University Hospital, 101 Daehangno, Jongno-gu, Seoul, 03080, Korea.
Ultrasound (US) is a widely used technique for liver disease but has limitations in distinguishing tumors. This study evaluates the clinical efficacy of fluctuational imaging (FLI), a new US method that detects the fluttering sign in liver tumors. We conducted a prospective exploratory study with 120 participants diagnosed with liver tumors through histopathology or standard imaging.
View Article and Find Full Text PDFInt J Biol Macromol
December 2024
School of Chemical Engineering, Changchun University of Technology, Changchun 130012, China.
In this study, we developed a novel composite catalytic hydrogel, which integrates excellent mechanical properties, catalytic activity, and sensing performance. Discarded hydrogel sensors are reused as templates for in-situ generation of metal nanoparticles, and multifunctional hydrogels combining sensing and catalysis are realized. Polyacrylamide (PAM) provides a three-dimensional network structure, while octadecyl methacrylate (SMA) acts as a hydrophobic association center, enhancing the structural stability of the hydrogel.
View Article and Find Full Text PDFNanomaterials (Basel)
December 2024
Center On Nanoenergy Research, Guangxi Key Laboratory for Relativistic Astrophysics, School of Physical Science and Technology, Guangxi University, Nanning 530004, China.
Wearable devices have potential applications in health monitoring and personalized healthcare due to their portability, conformability, and excellent mechanical flexibility. However, their performance is often limited by instability in acidic or basic environments. In this study, a flexible sensor with excellent stability based on a GaN nanoplate was developed through a simple and controllable fabrication process, where the linearity and stability remained at almost 99% of the original performance for 40 days in an air atmosphere.
View Article and Find Full Text PDFJ Imaging
December 2024
School of Innovation, Design and Technology (IDT), Mälardalen University, 72123 Västerås, Sweden.
As the demand for autonomous driving (AD) systems has increased, the enhancement of their safety has become critically important. A fundamental capability of AD systems is object detection and trajectory forecasting of vehicles and pedestrians around the ego-vehicle, which is essential for preventing potential collisions. This study introduces the Deep learning-based Acceleration-aware Trajectory forecasting (DAT) model, a deep learning-based approach for object detection and trajectory forecasting, utilizing raw sensor measurements.
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