Multinomial Bayesian Learning for Modeling Classical and Nonclassical Receptive Field Properties

DOI10.1162/NECO_a_00310zbMath1311.92045WikidataQ48564172 ScholiaQ48564172MaRDI QIDQ2919433

Haruo Hosoya

Publication date: 2 October 2012

Published in: Neural Computation (Search for Journal in Brave)

Mathematics Subject Classification ID

Neural biology (92C20)

Related Items (1)

How uncertainty bounds the shape index of simple cells

Cites Work

• Unnamed Item
• Unnamed Item
• Computational models of visual neurons specialised in the detection of periodic and aperiodic oriented visual stimuli: bar and grating cells
• Connectionist learning of belief networks
• Perceptual grouping and the interactions between visual cortical areas
• Computational theory and applications of a filling-in process at the blind spot
• Independent component analysis applied to feature extraction from colour and stereo images
• Simple-Cell-Like Receptive Fields Maximize Temporal Coherence in Natural Video

This page was built for publication: Multinomial Bayesian Learning for Modeling Classical and Nonclassical Receptive Field Properties