Mining structure-activity relations in biological neural networks using NeuronRank

Because it is too difficult to relate the structure of a cortical neural network to its dynamic activity analytically, we employ machine learning and data mining to learn structure-activity relations from sample random recurrent cortical networks and corresponding simulations. Inspired by the PageRank and the Hubs & Authorities algorithms for networked data, we introduce the NeuronRank algorithm, which assigns a source value and a sink value to each neuron in the network. Source and sink values are used as structural features for predicting the activity dynamics of biological neural networks. Our results show that NeuronRank based structural features can successfully predict average firing rates in the network, as well as the firing rate of output neurons reflecting the network population activity. They also indicate that link mining is a promising technique for discovering structure-activity relations in neural information processing. © 2007 Springer-Verlag Berlin Heidelberg.status: publishe