Temporal continuity of object identity is a feature of natural visual input and is potentially exploited - in an unsupervised manner - by the ventral visual stream to build the neural representation in inferior temporal (IT) cortex. Here, we investigated whether plasticity of individual IT neurons underlies human core object recognition behavioral changes induced with unsupervised visual experience. We built a single-neuron plasticity model combined with a previously established IT population-to-recognition-behavior-linking model to predict human learning effects. We found that our model, after constrained by neurophysiological data, largely predicted the mean direction, magnitude, and time course of human performance changes. We also found a previously unreported dependency of the observed human performance change on the initial task difficulty. This result adds support to the hypothesis that tolerant core object recognition in human and non-human primates is instructed - at least in part - by naturally occurring unsupervised temporal contiguity experience.
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http://dx.doi.org/10.7554/eLife.60830 | DOI Listing |
J Biol Rhythms
January 2025
Shiu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, California.
The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science and Technology, Donghua University, Shanghai, 201620, China.
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View Article and Find Full Text PDFPhys Chem Chem Phys
January 2025
Ural Federal University, Ekaterinburg, Russia.
This work is devoted to the study of the static magnetization of immobilized multi-core particles (MCPs) and their ensembles. These objects model aggregates of superparamagnetic nanoparticles that are taken up by biological cells and subsequently used, for example, as magnetoactive agents for cell imaging. In this study, we derive an analytical formula that allows us to predict the static magnetization of MCPs consisting of immobilized granules, in which the magnetic moment rotates freely the Néel mechanism.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Compared with conventional targets, small objects often face challenges such as smaller size, lower resolution, weaker contrast, and more background interference, making their detection more difficult. To address this issue, this paper proposes an improved small object detection method based on the YOLO11 model-PC-YOLO11s. The core innovation of PC-YOLO11s lies in the optimization of the detection network structure, which includes the following aspects: Firstly, PC-YOLO11s has adjusted the hierarchical structure of the detection network and added a P2 layer specifically for small object detection.
View Article and Find Full Text PDFJ Imaging
January 2025
Department of Precision Instrument, Tsinghua University, Beijing 100084, China.
The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. In physical 3D environments, existing adversarial attacks that target object detection (3D-AE) face significant challenges. These attacks often require large and dispersed modifications to objects, making them easily noticeable and reducing their effectiveness in real-world scenarios.
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