Results 21 to 30 of about 379,465 (333)

Global Jitter Motion of the Retinal Image Dynamically Alters the Receptive Field Properties of Retinal Ganglion Cells

open access: yesFrontiers in Neuroscience, 2019
Fixational eye movements induce aperiodic motion of the retinal image. However, it is not yet fully understood how fixational eye movements affect retinal information processing.
Akihiro Matsumoto   +4 more
doaj   +1 more source

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

open access: yesPLoS Computational Biology, 2016
Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and ...
Nicol S Harper   +5 more
doaj   +1 more source

Adaptation-dependent synchronous activity contributes to receptive field size change of bullfrog retinal ganglion cell. [PDF]

open access: yesPLoS ONE, 2012
Nearby retinal ganglion cells of similar functional subtype have a tendency to discharge spikes in synchrony. The synchronized activity is involved in encoding some aspects of visual input.
Hao Li, Wen-Zhong Liu, Pei-Ji Liang
doaj   +1 more source

Transformable Dilated Convolution by Distance for LiDAR Semantic Segmentation

open access: yesIEEE Access, 2022
LiDAR semantic segmentation is essential in autonomous vehicle safety. A rotating 3D LiDAR projects more laser points onto nearby objects and fewer points onto farther objects.
Jae-Seol Lee, Tae-Hyoung Park
doaj   +1 more source

Mapping sequences can bias population receptive field estimates

open access: yesNeuroImage, 2020
Population receptive field (pRF) modelling is a common technique for estimating the stimulus-selectivity of populations of neurons using neuroimaging. Here, we aimed to address if pRF properties estimated with this method depend on the spatio-temporal ...
Elisa Infanti, D. Samuel Schwarzkopf
doaj   +1 more source

Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual input.

open access: yesPLoS Computational Biology, 2013
Receptive fields acquired through unsupervised learning of sparse representations of natural scenes have similar properties to primary visual cortex (V1) simple cell receptive fields.
Jonathan J Hunt   +2 more
doaj   +1 more source

Nonlinear Hebbian learning as a unifying principle in receptive field formation [PDF]

open access: yes, 2016
The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the ...
Brito, Carlos S. N., Gerstner, Wulfram
core   +3 more sources

Does Corticothalamic Feedback Control Cortical Velocity Tuning? [PDF]

open access: yes, 2001
The thalamus is the major gate to the cortex and its contribution to cortical receptive field properties is well established. Cortical feedback to the thalamus is, in turn, the anatomically dominant input to relay cells, yet its influence on thalamic ...
Hillenbrand, Ulrich, van Hemmen, J. Leo
core   +5 more sources

Emergent spatial patterns of excitatory and inhibitory synaptic strengths drive somatotopic representational discontinuities and their plasticity in a computational model of primary sensory cortical area 3b

open access: yesFrontiers in Computational Neuroscience, 2016
Mechanisms underlying the emergence and plasticity of representational discontinuities in the mammalian primary somatosensory cortical representation of the hand are investigated in a computational model. The model consists of an input lattice organized
Kamil A. Grajski
doaj   +1 more source

Stereo Matching Algorithm Based on Atrous Convolution and Attention Module [PDF]

open access: yesJisuanji gongcheng, 2023
Most of the stereo matching algorithms based on convolutional neural networks require a large receptive field. However, the number of parameters in most algorithms is easy to increase when the receptive field is enlarged, which leads to high requirements
Zhihao LIU, Fanyun MENG, Jinhe WANG, Nan ZHANG
doaj   +1 more source

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