Connectivity estimation of neural networks using a spike train kernel

Estimating the connectivity strength based on the signals observed at each node of a network is an important task in neural network analysis. One notable example is estimating the connectivity of a biological neural network using spikes (i.e., action potentials) observed at electrodes. The research presented in this paper introduces a novel method that estimates the underlying connectivity of a given neural network based on a similarity measure applied to spike trains. Specifically, we use a normalized positive definite kernel defined on spike trains to estimate network connectivity. The proposed method was evaluated in the context of synthetic and real data. The generation of synthetic data is based on a CERM (Coupled Escape-Rate Model), which is known to generate spike trains of various types by tuning a few parameters. We also analyzed real data recorded from the visual cortex of an anaesthetized cat. The results showed that our method provides an effective way of estimating connectivity when spike trains are the only observable information.