  Assessing where and how information is stored in biological networks (such as
neuronal and genetic networks) is a central task both in neuroscience and in
molecular genetics, but most available tools focus on the network's structure
as opposed to its function. Here we introduce a new information-theoretic tool:
"information fragmentation analysis" that, given full phenotypic data, allows
us to localize information in complex networks, determine how fragmented
(across multiple nodes of the network) the information is, and assess the level
of encryption of that information. Using information fragmentation matrices, we
can also create information flow graphs that illustrate how information
propagates through these networks. We illustrate the use of this tool by
analyzing how artificial brains that evolved "in silico" solve particular
tasks, and show how information fragmentation analysis provides deeper insights
into how these brains process information and "think". The measures of
information fragmentation and encryption that result from our methods also
quantify complexity of information processing in these networks and how this
processing complexity differs between primary exposure to sensory data (early
in the lifetime) and later routine processing.
