Quantifying the amount, content and direction of communication between brain regions is key to understanding brain function. Traditional methods to analyze brain activity based on the Wiener-Granger causality principle quantify the overall information propagated by neural activity between simultaneously recorded brain regions, but do not reveal the information flow about specific features of interest (such as sensory stimuli). Here, we develop a new information theoretic measure termed Feature-specific Information Transfer (FIT), quantifying how much information about a specific feature flows between two regions. FIT merges the Wiener-Granger causality principle with information-content specificity. We first derive FIT and prove analytically its key properties. We then illustrate and test them with simulations of neural activity, demonstrating that FIT identifies, within the total information propagated between regions, the information that is transmitted about specific features. We then analyze three neural datasets obtained with different recording methods, magneto- and electro-encephalography, and spiking activity, to demonstrate the ability of FIT to uncover the content and direction of information flow between brain regions beyond what can be discerned with traditional analytical methods. FIT can improve our understanding of how brain regions communicate by uncovering previously unaddressed feature-specific information flow.

Public transportation is expensive to operate and maintain and is often unsatisfactory. The attractiveness of public transportation can be enhanced by making it more seamless, which, in turn, would reduce financial constraints and inefficiencies. The adoption of mobile devices for ticketing solutions is promising. However, current solutions are often inflexible and require manual interactions that produce evanescent data. Therefore, using leading-edge technologies and infrastructure, it is desirable to develop a solution to fully automate fare collection. In this paper, we provide a comprehensive literature review to understand the state of public transportation and to facilitate the development and implementation of automated fare collection solutions. First, we discuss existing mobile technologies and their common ticketing implementations. Second, we provide a predictive behavior model with sensor analytics to better understand customer needs. Finally, we highlight how machine learning can harness transactional ticketing data to create valuable business intelligence. Overall, developing and implementing automated fare collection solutions in urban transportation is expected to have a significant positive impact on customer experiences, the emergence of new business models and the reduction of pollutant emissions.