University of Wisconsin, Madison & Columbia University
Defining the neural substrates of consciousness
With this work, we
introduced a time- and state-dependent measure of integrated information, Φ, which captures the repertoire of causal states
available to a system as a whole. Specifically, Φ quantifies how much
information is generated (uncertainty is reduced) when a system enters a
particular state through causal interactions among its elements, above and beyond
the information generated independently by its parts. Such mathematical
characterization is motivated by the observation that integrated information
captures two key phenomenological properties of consciousness: (i) there is a
large repertoire of conscious experiences so that, when one particular
experience occurs, it generates a large amount of information by ruling out all
the others; and (ii) this information is integrated, in that each experience
appears as a whole that cannot be decomposed into independent parts. This paper
extends previous work on stationary systems and applies integrated information
to discrete networks as a function of their dynamics and causal architecture.
An analysis of basic examples indicates the following: (i) Φ varies depending
on the state entered by a network, being higher if active and inactive elements
are balanced and lower if the network is inactive or hyperactive. (ii) Φ varies
for systems with identical or similar surface dynamics depending on the
underlying causal architecture, being low for systems that merely copy or
replay activity states. (iii) Φ varies as a function of network
architecture. High Φ values can be obtained by architectures that
conjoin functional specialization with functional integration. Strictly modular
and homogeneous systems cannot generate high Φ because the former lack
integration, whereas the latter lack information. Feedforward and lattice
architectures are capable of generating high Φ but are inefficient. (iv)
In Hopfield networks, Φ is low for attractor states and neutral states,
but increases if the networks are optimized to achieve tension between local
and global interactions. These basic examples appear to match well against
neurobiological evidence concerning the neural substrates of consciousness.
More generally, Φ appears to be a useful metric to characterize the
capacity of any physical system to integrate information. This work has
been published in a paper in PLOS Computational Biology (Balduzzi & Tononi,
2008).