Evaluation of Associative Learning Based on Functional Connectivity in a Cultured Neuronal Network

In the brain, a large number of neurons interact with each other, and learning and memory take place while changing their functional structure. Therefore, in order to elucidate the mechanism of information processing in the brain, analysis focusing on functional connectivity between cells is necessary. In this study, we attempted to extract functional connections in the cultured neuronal network by repeated stimulation. In order to improve the accuracy of the extraction, we used a culture medium with a higher concentration of MgC1. As a result, the graph constructed by the extracted functional connections had small-world characteristics, indicating that this method may be effective for analyzing functional connections. In addition, we attempted to express the basic learning, associative learning, in the neuronal network. It was confirmed that repeated application of simultaneous stimuli enhanced the similarity of evoked responses by the two stimuli. These results indicate that learning is expressed by changes in functional connections, and that analysis of the specific changes in connections may help to elucidate the mechanism of information representation.