Neuronal reward valuations provide the physiological basis for economic behaviour. Yet, how such valuations are converted to economic decisions remains unclear. Here we show that the dorsolateral prefrontal cortex (DLPFC) implements a flexible value code based on object-specific valuations by single neurons. As monkeys perform a reward-based foraging task, individual DLPFC neurons signal the value of specific choice objects derived from recent experience. These neuronal object values satisfy principles of competitive choice mechanisms, track performance fluctuations and follow predictions of a classical behavioural model (Herrnstein's matching law). Individual neurons dynamically encode both, the updating of object values from recently experienced rewards, and their subsequent conversion to object choices during decision-making. Decoding from unselected populations enables a read-out of motivational and decision variables not emphasized by individual neurons. These findings suggest a dynamic single-neuron and population value code in DLPFC that advances from reward experiences to economic object values and future choices.
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http://dx.doi.org/10.1038/ncomms12554 | DOI Listing |
Sensors (Basel)
January 2025
Department of Civil Engineering and Engineering Management, National Quemoy University, Kinmen 89250, Taiwan.
Ground-based LiDAR technology has been widely applied in various fields for acquiring 3D point cloud data, including spatial coordinates, digital color information, and laser reflectance intensities (I-values). These datasets preserve the digital information of scanned objects, supporting value-added applications. However, raw point cloud data visually represent spatial features but lack attribute information, posing challenges for automated object classification and effective management.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electronic Engineering, Sogang University, Seoul 04107, Republic of Korea.
In advanced driver-assistance systems (ADASs), the misalignment of the mounting angle of the automotive radar significantly affects the accuracy of object detection and tracking, impacting system safety and performance. This paper introduces the Automotive Radar Alignment Detection Network (AutoRAD-Net), a novel model that leverages complex-valued convolutional neural network (CV-CNN) to address azimuth misalignment challenges in automotive radars. By utilizing complex-valued inputs, AutoRAD-Net effectively learns the physical properties of the radar data, enabling precise azimuth alignment.
View Article and Find Full Text PDFInsects
January 2025
School of InterNet, the National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei 230031, China.
Insect pests strongly affect crop growth and value globally. Fast and precise pest detection and counting are crucial measures in the management and mitigation of pest infestations. In this area, deep learning technologies have come to represent the method with the most potential.
View Article and Find Full Text PDFBeijing Da Xue Xue Bao Yi Xue Ban
February 2025
Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.
Objective: To quantitatively evaluate the accuracy of data obtained from liquid-interference surfaces using an intraoral 3D scanner (IOS) integrated with a compressed airflow system, so as to provide clinical proof of accuracy for the application of the compressed airflow system-based scanning head in improving data quality on liquid-interference surfaces.
Methods: The study selected a standard model as the scanning object, adhering to the "YY/T 1818-2022 Dental Science Intraoral Digital Impression Scanner" guidelines, a standard that defined parameters for intraoral scanning. To establish a baseline for accuracy, the ATOS Q 12M scanner, known for its high precision, was used to generate true reference values.
Nanomaterials (Basel)
January 2025
Institute of High Pressure Physics, Polish Academy of Sciences, Sokolowska 29/37, 01-142 Warsaw, Poland.
In situ X-ray reciprocal space mapping was performed during the interval heating and cooling of InGaN/GaN quantum wells (QWs) grown via metal-organic vapor phase epitaxy (MOVPE). Our detailed in situ X-ray analysis enabled us to track changes in the peak intensities and radial and angular broadenings of the reflection. By simulating the radial diffraction profiles recorded during the thermal cycle treatment, we demonstrate the presence of indium concentration distributions (ICDs) in the different QWs of the heterostructure (1.
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