Efficient combination of pairswise feature networks

TL;DR

This paper introduces a fast, unsupervised method for reconstructing neural network connectivity from calcium fluorescence data, enhancing existing techniques by better eliminating indirect links through a novel ensemble approach.

Contribution

It presents a new combination method that improves network reconstruction accuracy and speed over the state-of-the-art GTE method using normalization and ensemble of informative features.

Findings

Outperforms GTE in reconstructing neural networks

Effectively eliminates indirect links in network inference

Validated on simulated connectomics challenge data

Abstract

This paper presents a novel method for the reconstruction of a neural network connectivity using calcium fluorescence data. We introduce a fast unsupervised method to integrate different networks that reconstructs structural connectivity from neuron activity. Our method improves the state-of-the-art reconstruction method General Transfer Entropy (GTE). We are able to better eliminate indirect links, improving therefore the quality of the network via a normalization and ensemble process of GTE and three new informative features. The approach is based on a simple combination of networks, which is remarkably fast. The performance of our approach is benchmarked on simulated time series provided at the connectomics challenge and also submitted at the public competition.