https://github.com/d-dawg78/mva_neuro

Master MVA - Computational Neuroscience project
https://github.com/d-dawg78/mva_neuro

auditory-stimuli computational-neuroscience efficient-coding independent-component-analysis information-theory

Last synced: 9 days ago
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Master MVA - Computational Neuroscience project

• Host: GitHub
• URL: https://github.com/d-dawg78/mva_neuro
• Owner: d-dawg78
• Created: 2021-05-16T16:12:47.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-05-16T16:16:32.000Z (over 3 years ago)
• Last Synced: 2024-10-08T17:22:44.409Z (about 1 month ago)
• Topics: auditory-stimuli, computational-neuroscience, efficient-coding, independent-component-analysis, information-theory
• Language: Jupyter Notebook
• Homepage:
• Size: 2.85 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
#### Final Project Submission for the Computational Neuroscience course at the École Normale Supérieure (Master MVA)!

#### Group: Dorian Desblancs

#### Abstract:

In this project, we explore the field of efficient coding. We focus on the efficient coding of sounds using independent component analysis (ICA). The methodology used was heavily inspired by Michael S. Lewicki’s seminal paper Efficient Coding of Natural Sounds [16]. We used four sets of sounds, ordered from least to most periodic: environmental, speech, bird, and synthesizer. Our results demonstrate that the more periodic or harmonic a set of sounds is, the more sinusoidal its sources are. We notice that more random, environmental noises are split into components resembling wavelets. On the contrary, harmonic bird sounds are split into components resembling sinusoids. This work ties into the conclusions drawn by Lewicki. The center frequencies and bandwidths obtained by the set of derived auditory filters are similar to those found in the cochlear nervous system. This further reinforces the idea that information theory is linked to the brain, most notably in the realm of sparse coding.