The directional properties of parietal visual neurons (PVNs) were examined using the method of single-neuron analysis in waking monkeys. PVN properties were determined with passive visual stimuli as the animal executed a simple detection task. Parietal area PG was studied in 10 hemispheres of 6 male Macaca mulatta. Each class of parietal neurons was identified in PG: the fixation, projection, visual, and oculomotor neurons; 613 PVNs were identified, 323 were studied quantitatively, and 188 were studied with one or more of the protocols described. The receptive fields of PVNs are commonly large and bilateral, and at the limit some may fill the visual field; for many, the central zone of the visual field is spared when the fields are determined by stimuli that enter from the periphery and transit meridians. The receptive fields vary with the behavioral state, the angle of gaze, and the parameters of the stimuli used to determine them. PVNs are sensitive to stimulus movement but relatively insensitive to stimulus speed; many respond over a speed range of 5 degrees-500 degrees/sec. Stimulus-response relations may be incremental or decremental with increasing speed or show maxima or minima in the midrange of speed, but the response variation over the full range is rarely greater than 2:1. The directional preferences of PVNs with bilateral receptive fields are opponently organized; the preferred directions point either inward toward or outward away from the central line of gaze along the 4 meridians tested, which were equally spaced in the circular dimension of the visual field. The mechanism of the axis directionality of PVNs was studied using conditioning-test paradigms. They revealed a feed-forward inhibition preceding a stimulus, an effect that extends from the leading edge of the stimulus for 10 degrees-20 degrees in front of the moving stimulus and lasts for several hundred milliseconds. A double-Gaussian model of superimposed but unequal excitatory and inhibitory effects suffices to explain the present observations. It places demand upon the projection of functional properties from the contralateral hemisphere or from the ipsilateral prestriate areas that project upon PG over multistaged pathways and minimal demands upon intracortical processing mechanisms.
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http://dx.doi.org/10.1523/JNEUROSCI.07-01-00154.1987 | DOI Listing |
Magn Reson Med
January 2025
Department 8.1 - Biomedical Magnetic Resonance, Physikalisch-Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany.
Purpose: To develop a low-cost, high-performance, versatile, open-source console for low-field MRI applications that can integrate a multitude of different auxiliary sensors.
Methods: A new MR console was realized with four transmission and eight reception channels. The interface cards for signal transmission and reception are installed in PCI Express slots, allowing console integration in a commercial PC rack.
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View Article and Find Full Text PDFSensors (Basel)
January 2025
Mechnical and Vehicle Engineering, Hunan University, Changsha 411082, China.
Chip defect detection is a crucial aspect of the semiconductor production industry, given its significant impact on chip performance. This paper proposes a lightweight neural network with dual decoding paths for LED chip segmentation, named LDDP-Net. Within the LDDP-Net framework, the receptive field of the MobileNetv3 backbone is modified to mitigate information loss.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Electronic and Information Engineering, Ankang University, Ankang 725000, China.
Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information.
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