Topic: individual neurons

David Dahmen examines how differences between individual neurons affect network function and when homogeneous models miss important behaviour.

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes -- in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm which allows it to solve what we call the bisected graph matching problem. This modification allo

We present, for the first time, insights into functional synchronization of individual neurons under a complete anatomical neural connectivity map at a massive scale. While functional synchrony has been extensively studied as a mechanism of information processing in the brain, previous work has been limited to small populations of neurons in vitro, analyses across entire brain regions, or simulations in silico. Here, we leverage the large scale connectomics dataset MICrONS to analyze synchrony through the proxy of Pearson correlation across neurons at a larger scale in vivo with direct anatomi