Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Link mining: a new data mining challenge
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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It is difficult to relate the structure of a cortical neural network to its dynamic activity analytically. Therefore we employ machine learning and data mining algorithms to learn these relations from sample random recurrent cortical networks and corresponding simulations. Inspired by the PageRank and the Hubs & Authorities algorithms, we introduce the NeuronRank algorithm, which assigns a source value and a sink value to each neuron in the network. We show its usage to extract structural features from a network for the successful prediction of its activity dynamics. Our results show that NeuronRank features can successfully predict average firing rates in the network, and the firing rate of output neurons reflecting the network population activity.