structure-activity-nonlinear-spiking-networks

You can predict population activity from connectivity in nonlinear spiking networks by applying a diagrammatic fluctuation expansion framework to known connectivity and neuron nonlinearities to derive analytical correlations.

A diagrammatic fluctuation expansion is a framework that links connectome structure to collective population activity in nonlinear spiking neurons, addressing limitations of linearization by capturing realistic nonlinear spike response behavior.

Yes, you can compute both pairwise and higher-order correlations from connectome structure by applying the diagrammatic fluctuation expansion to your known network connectivity and specified neuron nonlinearities.

Yes, the framework includes cell-type aware extensions designed to study inhibitory and excitatory interactions across different neuron classes within nonlinear spiking networks.

Diagrammatic expansion is used instead of linearization because it connects connectome structure with population activity while capturing realistic nonlinear spike response behavior that linear approximations fail to model.

You need to define your network connectivity and neuron nonlinearities, then apply the diagrammatic fluctuation expansion to analyze how that structure shapes pairwise and higher-order population correlations.