A fundamental question concerning representation of the visual world in our brain is how a cortical cell responds when presented with more than a single stimulus. We find supportive evidence that most cells presented with a pair of stimuli respond predominantly to one stimulus at a time, rather than a weighted average response. Traditionally, the firing rate is assumed to be a weighted average of the firing rates to the individual stimuli (response-averaging model) (Bundesen et al., 2005). Here, we also evaluate a probability-mixing model (Bundesen et al., 2005), where neurons temporally multiplex the responses to the individual stimuli. This provides a mechanism by which the representational identity of multiple stimuli in complex visual scenes can be maintained despite the large receptive fields in higher extrastriate visual cortex in primates. We compare the two models through analysis of data from single cells in the middle temporal visual area (MT) of rhesus monkeys when presented with two separate stimuli inside their receptive field with attention directed to one of the two stimuli or outside the receptive field. The spike trains were modeled by stochastic point processes, including memory effects of past spikes and attentional effects, and statistical model selection between the two models was performed by information theoretic measures as well as the predictive accuracy of the models. As an auxiliary measure, we also tested for uni- or multimodality in interspike interval distributions, and performed a correlation analysis of simultaneously recorded pairs of neurons, to evaluate population behavior.
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http://dx.doi.org/10.3389/fncom.2016.00141 | DOI Listing |
J Comput Neurosci
January 2025
Program in Neuroscience, Indiana University Bloomington, Bloomington, IN, USA.
Hippocampal representations of space and time seem to share a common coding scheme characterized by neurons with bell-shaped tuning curves called place and time cells. The properties of the tuning curves are consistent with Weber's law, such that, in the absence of visual inputs, width scales with the peak time for time cells and with distance for place cells. Building on earlier computational work, we examined how neurons with such properties can emerge through self-supervised learning.
View Article and Find Full Text PDFQuant Imaging Med Surg
January 2025
School of Computer and Control Engineering, Yantai University, Yantai, China.
Background: Skin lesion segmentation plays a significant role in skin cancer diagnosis. However, due to the complex shapes, varying sizes, and different color depths, precise segmentation of skin lesions is a challenging task. Therefore, the aim of this study was to design a customized deep learning (DL) model for the precise segmentation of skin lesions, particularly for complex shapes and small target lesions.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Hunan University of Technology, Tianyuan District, Zhuzhou, 412007, China.
The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention mechanisms. Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters.
View Article and Find Full Text PDFFront Bioeng Biotechnol
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Department of Rheumatology and Immunology, Beijing Hospital, National Centre of Gerontology, Beijing, China.
Background: Knee osteoarthritis (KOA) constitutes the prevailing manifestation of arthritis. Radiographs function as a common modality for primary screening; however, traditional X-ray evaluation of osteoarthritis confronts challenges such as reduced sensitivity, subjective interpretation, and heightened misdiagnosis rates. The objective of this investigation is to enhance the validation and optimization of accuracy and efficiency in KOA assessment by utilizing fusion deep learning techniques.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Software Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Accurate 3D point cloud object detection is crucially important for autonomous driving vehicles. The sparsity of point clouds in 3D scenes, especially for smaller targets like pedestrians and bicycles that contain fewer points, makes detection particularly challenging. To solve this problem, we propose a single-stage voxel-based 3D object detection method, namely PFENet.
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