Statistical Inference of Functional Connectivity in Neuronal Networks using Frequent Episodes

TL;DR

This paper introduces a statistical framework using frequent episode discovery to identify significant spatio-temporal patterns in neuronal data, enabling estimation of connection strengths and network reconstruction.

Contribution

It presents a novel statistical model for significance testing and strength estimation of neural patterns using frequent episodes, improving resolution and network analysis capabilities.

Findings

Model accurately estimates connection strengths

Method successfully identifies significant patterns in simulated data

Application to cortical neuron data reveals meaningful network structures

Abstract

Identifying the spatio-temporal network structure of brain activity from multi-neuronal data streams is one of the biggest challenges in neuroscience. Repeating patterns of precisely timed activity across a group of neurons is potentially indicative of a microcircuit in the underlying neural tissue. Frequent episode discovery, a temporal data mining framework, has recently been shown to be a computationally efficient method of counting the occurrences of such patterns. In this paper, we propose a framework to determine when the counts are statistically significant by modeling the counting process. Our model allows direct estimation of the strengths of functional connections between neurons with improved resolution over previously published methods. It can also be used to rank the patterns discovered in a network of neurons according to their strengths and begin to reconstruct the graph structure of the network that produced the spike data. We validate our methods on simulated data and present analysis of patterns discovered in data from cultures of cortical neurons.