A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.
about
Efficient coding of spectrotemporal binaural sounds leads to emergence of the auditory space representation.Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.A structured model of video reproduces primary visual cortical organisation.A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields.Efficient sparse coding in early sensory processing: lessons from signal recoveryPopulation-wide distributions of neural activity during perceptual decision-making.Sparse coding can predict primary visual cortex receptive field changes induced by abnormal visual inputAre v1 simple cells optimized for visual occlusions? A comparative study.Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.Visual nonclassical receptive field effects emerge from sparse coding in a dynamical systemSparse coding in striate and extrastriate visual cortex.Spatio-chromatic adaptation via higher-order canonical correlation analysis of natural imagesModeling Inhibitory Interneurons in Efficient Sensory Coding Models.Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.Convergence and rate analysis of neural networks for sparse approximation.Experience-driven formation of parts-based representations in a model of layered visual memory.Perceptual decision making "through the eyes" of a large-scale neural model of v1.A common network architecture efficiently implements a variety of sparsity-based inference problems.Optimal sparse approximation with integrate and fire neurons.Competition improves robustness against loss of information.Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network.Role of homeostasis in learning sparse representations.Categorically distinct types of receptive fields in early visual cortex.Dual roles for spike signaling in cortical neural populations.Unsupervised learning of generative and discriminative weights encoding elementary image components in a predictive coding model of cortical function.Classification using sparse representations: a biologically plausible approach.Neocortical layer 4 as a pluripotent function linearizer.Forming cooperative representations via solipsistic synaptic plasticity rules.Discrete Sparse Coding.Sparse coding via thresholding and local competition in neural circuits.Receptive field self-organization in a model of the fine structure in v1 cortical columns.Learning invariance from natural images inspired by observations in the primary visual cortex.Anatomical constraints on lateral competition in columnar cortical architectures.Relative spike time coding and STDP-based orientation selectivity in the early visual system in natural continuous and saccadic vision: a computational model.The Two-Dimensional Gabor Function Adapted to Natural Image Statistics: A Model of Simple-Cell Receptive Fields and Sparse Structure in Images.Short-term memory capacity in networks via the restricted isometry property.Dead leaves and the dirty ground: low-level image statistics in transmissive and occlusive imaging environments.Efficient coding correlates with spatial frequency tuning in a model of V1 receptive field organization.Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.Extracting neuronal functional network dynamics via adaptive Granger causality analysis.
P2860
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P2860
A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields.
description
2007 nî lūn-bûn
@nan
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
2007年學術文章
@zh-hant
name
A network that uses few active ...... of cortical receptive fields.
@en
A network that uses few active ...... of cortical receptive fields.
@nl
type
label
A network that uses few active ...... of cortical receptive fields.
@en
A network that uses few active ...... of cortical receptive fields.
@nl
prefLabel
A network that uses few active ...... of cortical receptive fields.
@en
A network that uses few active ...... of cortical receptive fields.
@nl
P1476
A network that uses few active ...... of cortical receptive fields.
@en
P2093
Friedrich T Sommer
Martin Rehn
P2888
P304
P356
10.1007/S10827-006-0003-9
P577
2007-04-01T00:00:00Z