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Plasticity is one of the most important properties of the nervous system,which enables animals to adjust their behavior to the ever-changing externalenvironment. Changes in synaptic efficacy between neurons constitute one of themajor mechanisms of plasticity. Therefore, estimation of neural connections iscrucial for investigating information processing in the brain. Although manyanalysis methods have been proposed for this purpose, most of them suffer fromone or all the following mathematical difficulties: (1) only partially observedneural activity is available; (2) correlations can include both direct andindirect pseudo-interactions; and (3) biological evidence that a neurontypically has only one type of connection (excitatory or inhibitory) should beconsidered. To overcome these difficulties, a novel probabilistic framework forestimating neural connections from partially observed spikes is proposed inthis paper. First, based on the property of a sum of random variables, theproposed method estimates the influence of unobserved neurons on observedneurons and extracts only the correlations among observed neurons. Second, therelationship between pseudo-correlations and target connections is modeled byneural propagation in a multiplicative manner. Third, a novelinformation-theoretic framework is proposed for estimating neuron types. Theproposed method was validated using spike data generated by artificial neuralnetworks. In addition, it was applied to multi-unit data recorded from the CA1area of a rat's hippocampus. The results confirmed that our estimates areconsistent with previous reports. These findings indicate that the proposedmethod is useful for extracting crucial interactions in neural signals as wellas in other multi-probed point process data.

Estimation of neural connections from partially observed neural spikes