A connectome manipulation framework for the systematic and reproducible study of structure-function relationships through simulations.

ABSTRACT: Synaptic connectivity at the neuronal level is characterized by highly nonrandom features. Hypotheses about their role can be developed by correlating structural metrics to functional features. But, to prove causation, manipulations of connectivity would have to be studied. However, the fine-grained scale at which nonrandom trends are expressed makes this approach challenging to pursue experimentally. Simulations of neuronal networks provide an alternative route to study arbitrarily complex manipulations in morphologically and biophysically detailed models. Here, we present Connectome-Manipulator, a Python framework for rapid connectome manipulations of large-scale network models in Scalable Open Network Architecture TemplAte (SONATA) format. In addition to creating or manipulating the con

SUBMITTER: Pokorny C 

PROVIDER: S-EPMC11949583 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature