Abstract
The amount of publicly accessible experimental data has gradually increased in recent years, which makes it possible to reconsider many longstanding questions in neuroscience. In this paper, an efficient framework is presented for reconstructing functional connectivity using experimental spike-train data. A modified generalized linear model (GLM) with L1-norm penalty was used to investigate 10 datasets. These datasets contain spike-train data collected from the entorhinal-hippocampal region in the brains of rats performing different tasks. The analysis shows that entorhinal-hippocampal network of well-trained rats demonstrated significant small-world features. It is found that the connectivity structure generated by distance-dependent models is responsible for the observed small-world features of the reconstructed networks. The models are utilized to simulate a subset of units recorded from a large biological neural network using multiple electrodes. Two metrics for quantifying the small-world-ness both suggest that the reconstructed network from the sampled nodes estimates a more prominent small-world-ness feature than that of the original unknown network when the number of recorded neurons is small. Finally, this study shows that it is feasible to adjust the estimated small-world-ness results based on the number of neurons recorded to provide a more accurate reference of the network property.
Highlights
Developing mathematical models to describe how a mammalian nervous system functions is crucial to neuroscience, medicine, and bio-engineering
The reconstructed networks in this study show that the neural network in the entorhinal-hippocampal region of well-trained rats demonstrate small-world features, which are consistent with some previous studies[17,18]
We focused on the entorhinal-hippocampal region at the level of cells when a rat was performing a task for several rats with different tasks
Summary
Developing mathematical models to describe how a mammalian nervous system functions is crucial to neuroscience, medicine, and bio-engineering. To investigate the brain activities on the neuronal level, only limited portions of brain tissues could be analyzed even using the best available invasive techniques[5,6] Such techniques, e.g. multi-electrodes commonly used in recording electrophysiology, are sampling only some of the neurons from a large regional network. We evaluated the performance of two metrics for quantifying the small-world-ness of the sampled network and simulated a large network using the distance-dependent probability model. Based on the topology of the recorded neurons, we evaluated the small-world-ness of the functional network. For a more precise reference of the network property, we developed a means to adjust the small-world-ness measure based on the number of sampled neurons. As the ability to control the reconstructed network is critical to guide the dynamics of the whole network, we have studied network reconstructed from real datasets to find the “key” nodes to structurally control the entire system
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