One leading hypothesis on the nature of visual callosal connections (CC) is that they replicate features of intrahemispheric lateral connections. However, CC act also in the central part of the binocular visual field. In agreement, early experiments in cats indicated that they provide the ipsilateral eye part of binocular receptive fields (RFs) at the vertical midline (Berlucchi and Rizzolatti, 1968), and play a key role in stereoscopic function. But until today callosal inputs to receptive fields activated by one or both eyes were never compared simultaneously, because callosal function has been often studied by cutting or lesioning either corpus callosum or optic chiasm not allowing such a comparison. To investigate the functional contribution of CC in the intact cat visual system we recorded both monocular and binocular neuronal spiking responses and receptive fields in the 17/18 transition zone during reversible deactivation of the contralateral hemisphere. Unexpectedly from many of the previous reports, we observe no change in ocular dominance during CC deactivation. Throughout the transition zone, a majority of RFs shrink, but several also increase in size. RFs are significantly more affected for ipsi- as opposed to contralateral stimulation, but changes are also observed with binocular stimulation. Noteworthy, RF shrinkages are tiny and not correlated to the profound decreases of monocular and binocular firing rates. They depend more on orientation and direction preference than on eccentricity or ocular dominance of the receiving neuron's RF. Our findings confirm that in binocularly viewing mammals, binocular RFs near the midline are constructed via the direct geniculo-cortical pathway. They also support the idea that input from the two eyes complement each other through CC: Rather than linking parts of RFs separated by the vertical meridian, CC convey a modulatory influence, reflecting the feature selectivity of lateral circuits, with a strong cardinal bias.
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http://dx.doi.org/10.3389/fnsys.2018.00011 | DOI Listing |
Elife
January 2025
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January 2025
Department of Psychological and Brain Sciences, Boston University, Boston, MA, 02215.
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School of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Electrocardiogram (ECG) signals contain complex and diverse features, serving as a crucial basis for arrhythmia diagnosis. The subtle differences in characteristics among various types of arrhythmias, coupled with class imbalance issues in datasets, often hinder existing models from effectively capturing key information within these complex signals, leading to a bias towards normal classes. To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN).
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College of Engineering, Huaqiao University, Quanzhou 362021, China.
Grasping objects of irregular shapes and various sizes remains a key challenge in the field of robotic grasping. This paper proposes a novel RGB-D data-based grasping pose prediction network, termed Cascaded Feature Fusion Grasping Network (CFFGN), designed for high-efficiency, lightweight, and rapid grasping pose estimation. The network employs innovative structural designs, including depth-wise separable convolutions to reduce parameters and enhance computational efficiency; convolutional block attention modules to augment the model's ability to focus on key features; multi-scale dilated convolution to expand the receptive field and capture multi-scale information; and bidirectional feature pyramid modules to achieve effective fusion and information flow of features at different levels.
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December 2024
College of Electrical Engineering, Sichuan University, Chengdu 610065, China.
Remote photo-plethysmography (rPPG) is a useful camera-based health motioning method that can measure the heart rhythm from facial videos. Many well-established deep learning models can provide highly accurate and robust results in measuring heart rate (HR) and heart rate variability (HRV). However, these methods are unable to effectively eliminate illumination variation and motion artifact disturbances, and their substantial computational resource requirements significantly limit their applicability in real-world scenarios.
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