1. bookVolume 7 (2017): Issue 1 (January 2017)
Journal Details
First Published
30 Dec 2014
Publication timeframe
4 times per year
access type Open Access

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

Published Online: 17 Dec 2016
Page range: 17 - 31
Journal Details
First Published
30 Dec 2014
Publication timeframe
4 times per year

Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike trains (sequences of action potentials) are observed at each neuron, but the way in which these neurons are connected is unknown. This paper introduces a novel method for estimating connections between nodes using a similarity measure between sequences of event times. Specifically, a normalized positive definite kernel defined on spike trains was used. The proposed method was evaluated using synthetic and real data, by comparing with methods using transfer entropy and the Victor-Purpura distance. Synthetic data was generated using CERM (Coupled Escape-Rate Model), a model that generates various spike trains. Real data recorded from the visual cortex of an anaesthetized cat was analyzed as well. The results showed that the proposed method provides an effective way of estimating the connectivity of a network when the time sequences of events are the only available information.


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