# Emergent stochastic oscillations and signal detection in tree networks of excitable elements

## TL;DR

This paper investigates how stochastic oscillations and signal detection emerge in tree networks of excitable elements, revealing how network topology influences spike train statistics and sensory signal processing.

## Contribution

It demonstrates that in strongly-coupled tree networks, spike train statistics can be predicted from a single excitable element with rescaled parameters, and shows how topology tuning affects signal detection.

## Key findings

- Spike train statistics can be predicted from an isolated element with rescaled parameters.
- Network topology influences firing rate and variability.
- Optimal input discrimination can be achieved by tuning network topology.

## Abstract

We study the stochastic dynamics of strongly-coupled excitable elements on a tree network. The peripheral nodes receive independent random inputs which may induce large spiking events propagating through the branches of the tree and leading to global coherent oscillations in the network. This scenario may be relevant to action potential generation in certain sensory neurons, which possess myelinated distal dendritic tree-like arbors with excitable nodes of Ranvier at peripheral and branching nodes and exhibit noisy periodic sequences of action potentials. We focus on the spiking statistics of the central node, which fires in response to a noisy input at peripheral nodes. We show that, in the strong coupling regime, relevant to myelinated dendritic trees, the spike train statistics can be predicted from an isolated excitable element with rescaled parameters according to the network topology. Furthermore, we show that by varying the network topology the spike train statistics of the central node can be tuned to have a certain firing rate and variability, or to allow for an optimal discrimination of inputs applied at the peripheral nodes.