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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...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Public Library of Science
2017
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| 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 |