Computational inference of synaptic polarities in neuronal networks

Synaptic polarity, i.e. whether synapses are inhibitory (-) or excitatory (+), is challenging to map, although being a key to understand brain function. Here, synaptic polarity is inferred with high precision considering three experimental scenarios. First, using the C. elegans connectome as an example, detailed neurotransmitter (NT) and receptor (R) gene expression is assumed. Such existing datasets are linked by the Connectome Model (CM), using a wiring rule network summarizing how NT-R pairs govern synaptic polarity, resolving 356 synaptic polarities in addition to the 1,752 known polarities. Second, known synaptic polarities are considered as an input, in addition to the NT and R expression data. Even without any wiring rules as an input, the Spatial Connectome Model (SCM) recovers 72% of the CM-resolved pairs at a threshold corresponding to >95% precision, while also inferring 118 of the remaining unknown polarities. Last, when no genetic information is available, the generalized Connectome Model (GCM) is introduced and compared to signed generalizations of network-based link prediction methods to infer synaptic polarities. Our results address current challenges in unveiling large-scale synaptic polarities, an essential step towards more realistic dynamical brain models.