Minimum Mean Square Error Estimation of Connectivity in Biological Neural Networks

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dc.contributor.authorYang, X.en_US
dc.contributor.authorShamma, S.A.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:48:27Z
dc.date.available2007-05-23T09:48:27Z
dc.date.issued1991en_US
dc.description.abstractA minimum mean square error (MMSE) estimation scheme is employed to identify the synaptic connectivity in neural networks. This new approach can substantially reduce the amount of data and the computational cost involved in the conventional correlation methods, and is suitable for both nonstationary and stationary neuronal firings. Two algorithms are proposed to estimate the synaptic connectivities recursively, one for nonlinear filtering, the other for linear filtering. In addition, the lower and upper bounds for the MMSE estimator are determined. It is shown that the estimators are consistent in quadratic mean. We also demonstrate that the conventional crossinterval histogram is an asymptotic linear MMSE estimator with an inappropriate initial value. Finally, simulations of both the nonlinear and linear (Kalman filter) estimates demonstrate that the true connectivity values are approached asymptotically.en_US
dc.format.extent849875 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/5120
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1991-72en_US
dc.subjectmathematical modelingen_US
dc.subjectneural networksen_US
dc.subjectdetectionen_US
dc.subjectestimationen_US
dc.subjectfilteringen_US
dc.subjectinformation theoryen_US
dc.subjectneural systemsen_US
dc.subjectsignal processingen_US
dc.subjectalgorithmsen_US
dc.subjectcomputational complexityen_US
dc.subjectCommunication en_US
dc.subjectSignal Processing Systemsen_US
dc.titleMinimum Mean Square Error Estimation of Connectivity in Biological Neural Networksen_US
dc.typeTechnical Reporten_US

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