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The characterisation of neuronal connectivity is one of the most importantmatters in neuroscience. In this work, we show that a recently proposedinformational quantity, the causal mutual information, employed with anappropriate methodology, can be used not only to correctly infer the directionof the underlying physical synapses, but also to identify their excitatory orinhibitory nature, considering easy to handle and measure bivariatetime-series. The success of our approach relies on a surprising property foundin neuronal networks by which non-adjacent neurons do "understand" each other(positive mutual information), however this exchange of information is notcapable of causing effect (zero transfer entropy). Remarkably, inhibitoryconnections, responsible for enhancing synchronisation, transfer moreinformation than excitatory connections, known to enhance entropy in thenetwork. We also demonstrate that our methodology can be used to correctlyinfer directionality of synapses even in the presence of dynamic andobservational Gaussian noise, and is also successful in providing the effectivedirectionality of inter modular connectivity, when only mean fields can bemeasured.

Inference of topology and the nature of synapses, and the flow of information in neuronal networks