# Intertangled stochastic motifs in networks of excitatory-inhibitory units

## TL;DR

This paper introduces a stochastic model of excitatory-inhibitory networks demonstrating how intrinsic noise induces long-range correlations and patterns, offering insights into complex biological computations.

## Contribution

The study reveals how endogenous noise in excitatory-inhibitory networks generates persistent long-range correlations and stochastic patterns absent in deterministic models.

## Key findings

- Endogenous noise drives robust inter-node correlations.
- Anti-phase synchrony promotes long-range stochastic patterns.
- Patterns emerge only with stochastic effects, not in deterministic models.

## Abstract

A stochastic model of excitatory and inhibitory interactions which bears universality traits is introduced and studied. The endogenous component of noise, stemming from finite size corrections, drives robust inter-nodes correlations, that persist at large large distances. Anti-phase synchrony at small frequencies is resolved on adjacent nodes and found to promote the spontaneous generation of long-ranged stochastic patterns, that invade the network as a whole. These patterns are lacking under the idealized deterministic scenario, and could provide novel hints on how living systems implement and handle a large gallery of delicate computational tasks.