Learning probabilistic models of connectivity from multiple spike train data
© Patnaik et al; licensee BioMed Central Ltd. 2010
Published: 20 July 2010
Neuronal circuits or cell assemblies carry out brain function through complex coordinated firing patterns . Inferring topology of neuronal circuits from simultaneously recorded spike train data is a challenging problem in neuroscience. In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons . The emphasis on excitatory networks allows us to learn connectivity models by exploiting fast data mining algorithms. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and can be summarized into a dynamic Bayesian network (DBN).
We demonstrate the effectiveness of our method in discovering connectivity information on synthetic and real datasets. Our synthetic data generation models each neuron as an inhomogeneous Poisson process whose firing rate is modulated by the input received by the neuron in recent past. The network inter-connect allows us to model complex higher-order interactions. We also demonstrate the application of our method on multi-electrode arrays recordings from dissociated cortical cultures gathered by Steve Potter's laboratory at Georgia Tech .
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