Receptive Field Self-Organization in a Model of the Fine Structure in V1 Cortical Columns

被引:17
作者
Luecke, Joerg [1 ,2 ]
机构
[1] UCL, Gatsby Computat Neurosci Unit, London WC1N 3AR, England
[2] Goethe Univ Frankfurt, Frankfurt Inst Adv Studies, D-60438 Frankfurt, Germany
关键词
PYRAMIDAL CELL MODULES; LONG-TERM POTENTIATION; PRIMARY VISUAL-CORTEX; ORIENTATION SELECTIVITY; INDEPENDENT COMPONENTS; CEREBRAL-CORTEX; SPARSE; PLASTICITY; NETWORK; REPRESENTATIONS;
D O I
10.1162/neco.2009.07-07-584
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We study a dynamical model of processing and learning in the visual cortex, which reflects the anatomy of V1 cortical columns and properties of their neuronal receptive fields. Based on recent results on the fine-scale structure of columns in V1, we model the activity dynamics in subpopulations of excitatory neurons and their interaction with systems of inhibitory neurons. We find that a dynamical model based on these aspects of columnar anatomy can give rise to specific types of computations that result in self-organization of afferents to the column. For a given type of input, self-organization reliably extracts the basic input components represented by neuronal receptive fields. Self-organization is very noise tolerant and can robustly be applied to different types of input. To quantitatively analyze the system's component extraction capabilities, we use two standard benchmarks: the bars test and natural images. In the bars test, the system shows the highest noise robustness reported so far. If natural image patches are used as input, self-organization results in Gabor-like receptive fields. In quantitative comparison with in vivo measurements, we find that the obtained receptive fields capture statistical properties of V1 simple cells that algorithms such as independent component analysis or sparse coding do not reproduce.
引用
收藏
页码:2805 / 2845
页数:41
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