Highlights d Adult-born neuron (ABN) activity during sleep can be seen using Ca 2+ imaging d ABNs active after learning reactivate in subsequent rapid eye movement (REM) sleep d Optogenetic manipulation of ABN activity in REM sleep impairs memory consolidation d This effect may be mediated by ABN synaptic plasticity

Firms build business relationships during economic activities. The goal of this paper is to clarify production mechanisms and economic functions by identifying characteristic patterns of inter-firm interactions. In this paper, we empirically analyze an inter-firm network consisting of about one million firms and four million directed links, in order to specify network motifs, which are small subgraphs that occur more frequently than expected in a randomly generated network. We found that V-shaped triads are network motifs, while feedforward and feedback loop are anti-motifs. By defining roles in the subgraph according to structural equivalence, we also detected the significance profile of roles characterizing the industry sector. The taxonomy of industries obtained from the profiles is economically meaningful. These empirical findings may serve to provide an easily interpretable view of the entire inter-firm network and to improve the efficiency and safety of economic systems. T. Ohnishi (B)

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Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.