  Functional connectome extends the anatomical connectome by capturing the
relations between neurons according to their activity and interactions. When
these relations are causal, the functional connectome maps how neural activity
flows within neural circuits and provides the possibility for inference of
functional neural pathways, such as sensory-motor-behavioral pathways. While
there exist various information approaches for non-causal estimations of the
functional connectome, approaches that characterize the causal functional
connectivity - the causal relationships between neuronal time series, are
scarce. In this work, we develop the Neuro-PC algorithm which is a novel
methodology for inferring the causal functional connectivity between neurons
from multi-dimensional time series, such as neuronal recordings. The core of
our methodology relies on a novel adaptation of the PC algorithm, a
state-of-the-art method for statistical causal inference, to the
multi-dimensional time-series of neural dynamics. We validate the performance
of the method on network motifs with various interactions between their neurons
simulated using continuous-time artificial network of neurons. We then consider
the application of the method to obtain the causal functional connectome for
recent multi-array electrophysiological recordings from the mouse visual cortex
in the presence of different stimuli. We show how features of the mapping can
be used for quantification of the similarities between neural responses subject
to different stimuli.
