How neuron physicality shapes the structure in biological neural networks

A strong hypothesis in neuroscience is that many aspects of brain function are determined by the ‘’map of the brain’’ and that its computational power relies on its connectivity architecture. Impressive scientific and engineering advances in recent years generated a plethora of large brain networks of incredibly complex architectures.

A central feature of the architecture is its inherent directionality, which reflects the flow of information. Evidence shows that reciprocal connections and higher order motifs, such as directed cliques, emerge selectively rather than at random in biological neural networks. This raises fundamental questions in both mathematics and computational neuroscience. In this talk, we explore how such structure arises from the physicality of the neurons themselves and propose a framework to control and quantify the over or under representation of higher order motifs.