  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.
