Deduces the functional neural network and synaptic connections. Parallel Dual NeuInf is based on the kernel method to map the nonlinear inference problem to a linear equivalent in the kernel space. It scales to large datasets of recorded neural activities. This tool can deal with both deterministic and stochastic LIf neurons through the same framework.
Inference, Information and Decision Systems Group, Yale Institute for Network Science, Yale University, New Haven, CT, USA; Laboratory of Audiovisual Communications (LCAV), School of Computer and Communication Sciences, Ecole Polytechnique Federale de Lausanne (EPFL), Lausanne, Switzerland