Experimental research over the last two decades has shown that the anatomical connectivity among neurons is largely non-random across brain areas. This complex organisation shapes the flow of information, giving rise to specific pathways and motifs, which are ultimately responsible for processes like emotions, cognitive functions and behaviour, just to mention few. Due to the spectacular progress of technology, the study of the brain wiring diagram, known as connectomics, has received considerable attention in recent years, resulting in the proliferation of large data sets. From one side, this adds a significant contribution towards a better understanding of the complex processes that take place in the brain. On the other side, however, analysing such large connectivities is a hard task that has not yet found a satisfactory solution. Particular evidence has been found for bidirectional motifs,occurring when two neurons project onto each other via connections of equal strength, and unidirectional motifs, when one of the two connections is dominant. These specific motifs were found to correlate with short-term synaptic plasticity properties, which are related to resources availability for signal transmission. The aim of this thesis is to add a contribution to the ongoing efforts spent on answering the two main questions related to motif evidence: How can we satisfactory detect and measure motifs in large networks and why do they have the characteristics that we observe? Following existing literature, we hypothesise that bidirectional and unidirectional motifs appear as a consequence of learning processes, which move the distribution of the synaptic connections away from randomness through activity dependent synaptic plasticity. Based on this, we introduce a symmetry measure for global connectivity and a statistics-based heuristic algorithm for directed and weighted graphs that is able to detect overlapping bidirectional communities within large networks. On the other side, to address the why question we introduce an error-driven learning framework for short-term plasticity that acts jointly with Spike-Timing Dependent Plasticity, a well-known learning mechanism for long-term plasticity: By allowing synapses to change their properties,neurons are able to adapt their own activity depending on an error signal. This results in more rich dynamics and also, provided that the learning mechanism is target-specific, leads to specialised groups of synapses projecting onto functionally different targets, qualitatively replicating the experimental results of Wang and collaborators in 2006.
