Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise

Accuracy Maximization Analysis (AMA) is a recently developed Bayesian ideal observer method for task-specific dimensionality reduction. Given a training set of proximal stimuli (e.g. retinal images), a response noise model, and a cost function, AMA returns the filters (i.e. receptive fields) that ex...

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Detalles Bibliográficos
Autores principales: Burge, Johannes, Jaini, Priyank
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298250/
https://www.ncbi.nlm.nih.gov/pubmed/28178266
http://dx.doi.org/10.1371/journal.pcbi.1005281

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5298250/
https://www.ncbi.nlm.nih.gov/pubmed/28178266
http://dx.doi.org/10.1371/journal.pcbi.1005281

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