# A study of dependency features of spike trains through copulas

## TL;DR

This paper introduces a novel method using copulas to analyze spike train dependencies, enabling detection of different connection types in neural networks from synthetic data.

## Contribution

It adapts copula-based analysis to distinguish direct and indirect neuron interactions, expanding tools for neural network structure detection.

## Key findings

- Copula shapes reveal different dependency types.
- Method differentiates direct from indirect neuron interactions.
- Effective on synthetic neural network data.

## Abstract

Simultaneous recordings from many neurons hide important information and the connections characterizing the network remain generally undiscovered despite the progresses of statistical and machine learning techniques. Discerning the presence of direct links between neuron from data is still a not completely solved problem. To enlarge the number of tools for detecting the underlying network structure, we propose here the use of copulas, pursuing on a research direction we started in [1]. Here, we adapt their use to distinguish different types of connections on a very simple network. Our proposal consists in choosing suitable random intervals in pairs of spike trains determining the shapes of their copulas. We show that this approach allows to detect different types of dependencies. We illustrate the features of the proposed method on synthetic data from suitably connected networks of two or three formal neurons directly connected or influenced by the surrounding network. We show how a smart choice of pairs of random times together with the use of empirical copulas allows to discern between direct and un-direct interactions.