A Neuronal Noise Critique of Integrated Information Theory

TL;DR

This paper critiques Integrated Information Theory by highlighting its inconsistency with experimental data on neuronal noise, emphasizing that noise plays a crucial role in brain functions like learning and recognition.

Contribution

It identifies a fundamental inconsistency in IIT's treatment of neuronal noise and argues for its reformulation to align with empirical evidence.

Findings

Neuronal noise is essential for learning and recognition.

IIT's prediction about noise reducing information integration is incorrect.

Experimental data contradicts IIT's assumptions about noise effects.

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.