Title: Can Transfer Entropy Infer Causality in Neuronal Circuits for Cognitive Processing?,
Abstract: Finding the causes to observed effects and establishing causal relationships
between events is (and has been) an essential element of science and
philosophy. Automated methods that can detect causal relationships would be
very welcome, but practical methods that can infer causality are difficult to
find, and the subject of ongoing research. While Shannon information only
detects correlation, there are several information-theoretic notions of
"directed information" that have successfully detected causality in some
systems, in particular in the neuroscience community. However, recent work has
shown that some directed information measures can sometimes inadequately
estimate the extent of causal relations, or even fail to identify existing
cause-effect relations between components of systems, especially if neurons
contribute in a cryptographic manner to influence the effector neuron. Here, we
test how often cryptographic logic emerges in an evolutionary process that
generates artificial neural circuits for two fundamental cognitive tasks:
motion detection and sound localization. Our results suggest that whether or
not transfer entropy measures of causality are misleading depends strongly on
the cognitive task considered. These results emphasize the importance of
understanding the fundamental logic processes that contribute to cognitive
processing, and quantifying their relevance in any given nervous system.