A Neuronal Noise Critique of Integrated Information Theory
Refath Bari
arXiv Preprint Archive December 6, 2021 preprint
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or philosophical paper |
|---|---|
| Topics | Philosophy of mind |
| Keywords | Q-bio.nc Cognitive science Neural computation |
| Key points | Argues that Integrated Information Theory's stance on neuronal noise is inconsistent with experimental evidence showing noise is necessary for learning and visual recognition. |
Abstract
Integrated Information Theory (IIT) is an audacious attempt to pin down the abstract, phenomenological experiences of consciousness into a rigorous, mathematical framework. We show that IIT's stance in regards to neuronal noise is inconsistent with experimental data demonstrating that neuronal noise in the brain plays a critical role in learning, visual recognition, and even categorical representation. IIT predicts that entropy due to noise will reduce the information integration of a physical system, which is inconsistent with experimental data demonstrating that decision-related noise is a necessary condition for learning and visual recognition tasks. IIT must therefore be reformulated to accommodate experimental evidence showing both the successes and failures of noise.
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