# From the statistics of connectivity to the statistics of spike times in neuronal networks

## TL;DR

This paper explores how the structure of neural networks influences their collective spike activity, revealing principles that link connectivity patterns to global activity statistics and the role of plasticity in this relationship.

## Contribution

It identifies broad principles connecting local connectivity features to global activity and examines how excitatory-inhibitory balance affects spike correlations in large networks.

## Key findings

- Local connectivity features predict global activity statistics
- Correlated spiking depends on spatial scales of connectivity
- Plasticity rules can link network structure to activity patterns

## Abstract

An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.