  The specific connectivity of a neuronal network is reflected in the dynamics
of the signals recorded on its nodes. The analysis of how the activity in one
node predicts the behaviour of another gives the directionality in their
relationship. However, each node is composed of many different elements which
define the properties of the links. For instance, excitatory and inhibitory
neuronal subtypes determine the functionality of the connection. Classic
indexes such as the Granger causality (GC) quantifies these interactions, but
they do not infer into the mechanism behind them. Here, we introduce an
extension of the well-known GC that analyses the correlation associated to the
specific influence that a transmitter node has over the receiver. This way, the
G-causal link has a positive or negative effect if the predicted activity
follows directly or inversely, respectively, the dynamics of the sender. The
method is validated in a neuronal population model, testing the paradigm that
excitatory and inhibitory neurons have a differential effect in the
connectivity. Our approach correctly infers the positive or negative coupling
produced by different types of neurons. Our results suggest that the proposed
approach provides additional information on the characterization of G-causal
connections, which is potentially relevant when it comes to understanding
interactions in the brain circuits.
