Learning Midlevel Auditory Codes from Natural Sound Statistics

DOI10.1162/NECO_A_01048zbMath1472.92033arXiv1701.07138OpenAlexW2582082042WikidataQ47926379 ScholiaQ47926379MaRDI QIDQ5157142

Wiktor Młynarski, Josh H. McDermott

Publication date: 12 October 2021

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

Full work available at URL: https://arxiv.org/abs/1701.07138

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Neural networks for/in biological studies, artificial life and related topics (92B20)

Uses Software

• DARPA TIMIT

Cites Work

• Natural image statistics. A probabilistic approach to early computational vision.
• The spectro-temporal receptive field. A functional characteristic of auditory neurons
• Imposing sparsity on the mixing matrix in independent component analysis.
• Sparse spectrotemporal coding of sounds
• A Hierarchical Bayesian Model for Learning Nonlinear Statistical Regularities in Nonstationary Natural Signals

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