Plasticity is one of the most important features of the nervous systems that enable animals to adjust their behavior to an ever-changing external world. A major mechanism of plasticity is the changes in synaptic efficacy between neurons , and therefore estimation of neural connections is crucial for investigating information processing in the brain. Although many analysis methods have been proposed to this end , most of them suffer from a mathematical difficulty that stems from the fact that activity of only a limited number of neurons are available. That is, many existing methods ignore effects from unobserved neurons. To overcome this difficulty , a novel probabilistic framework is proposed for estimating neural connections from partially observed spikes . In this study, we estimate influence on observed neurons from unobserved neurons based on probabilistic firing model and abstract the connections among observed neurons. Our numerical experiment supports usefulness of our proposed method .
Plasticity is one of the most important properties of the nervous system, which enables animals to adjust their behavior to the ever-changing external environment. Changes in synaptic efficacy between neurons constitute one of the major mechanisms of plasticity. Therefore, estimation of neural connections is crucial for investigating information processing in the brain. Although many analysis methods have been proposed for this purpose , most of them suffer from one or all the following mathematical difficulties: (1) only partially observed neural activity is available; (2) correlations can include both direct and indirect pseudo-interactions; and (3) biological evidence that a neuron typically has only one type of connection (excitatory or inhibitory) should be considered. To overcome these difficulties , a novel probabilistic framework for estimating neural connections from partially observed spikes is proposed in this paper. First, based on the property of a sum of random variables, the proposed method estimates the influence of unobserved neurons on observed neurons and extracts only the correlations among observed neurons. Second, the relationship between pseudo-correlations and target connections is modeled by neural propagation in a multiplicative manner. Third, a novel information-theoretic framework is proposed for estimating neuron types. The proposed method was validated using spike data generated by artificial neural networks. In addition, it was applied to multi-unit data recorded from the CA1 area of a rat's hippocampus. The results confirmed that our estimates are consistent with previous reports. These findings indicate that the proposed method is useful for extracting crucial interactions in neural signals as well as in other multi-probed point process data .
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