<abstract xmlns="http://www.w3.org/1999/xhtml"><p> 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.</p></abstract>

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.

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

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike...

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

Publié en ligne: 17 déc. 2016

Pages: 17 - 31

DOI: https://doi.org/10.1515/jaiscr-2017-0002

Mots clés
connectivity estimation, neural network, kernel methods, spike train

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Publié en ligne: 17 déc. 2016

Pages: 17 - 31

DOI: https://doi.org/10.1515/jaiscr-2017-0002

Mots clésconnectivity estimation, neural network, kernel methods, spike train

© 2016

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
connectivity estimation, neural network, kernel methods, spike train