Identification of functional information subgraphs in complex networks

We present a general information theoretic approach for identifying functional subgraphs in complex networks where the dynamics of each node are observable. We show that the uncertainty in the state of each node can be expressed as a sum of information quantities involving a growing number of correlated variables at other nodes. We demonstrate that each term in this sum is generated by successively conditioning mutual informations on new measured variables, in a way analogous to a discrete differential calculus. The analogy to a Taylor series suggests efficient search algorithms for determining the state of a target variable in terms of functional groups of other degrees of freedom. We apply this methodology to electrophysiological recordings of networks of cortical neurons grown it in vitro. Despite strong stochasticity, we show that each cell's patterns of firing are generally explained by the activity of a small number of other neurons. We identify these neuronal subgraphs in terms of their mutually redundant or synergetic character and reconstruct neuronal circuits that account for the state of each target cell.Comment: 4 pages, 4 figure

Visualizing classification of natural video sequences using sparse, hierarchical models of cortex.

Recent work on hierarchical models of visual cortex has reported state-of-the-art accuracy on whole-scene labeling using natural still imagery. This raises the question of whether the reported accuracy may be due to the sophisticated, non-biological back-end supervised classifiers typically used (support vector machines) and/or the limited number of images used in these experiments. In particular, is the model classifying features from the object or the background? Previous work (Landecker, Brumby, et al., COSYNE 2010) proposed tracing the spatial support of a classifier’s decision back through a hierarchical cortical model to determine which parts of the image contributed to the classification, compared to the positions of objects in the scene. In this way, we can go beyond standard measures of accuracy to provide tools for visualizing and analyzing high-level object classification. We now describe new work exploring the extension of these ideas to detection of objects in video sequences of natural scenes

How seriously should we take the opinions of academics and experts when it comes to complicated issues like electoral integrity?

The result of the 2015 General Election came as a surprise for most people, but particularly those in the academic and polling community. But what is the appropriate role for academics in an electoral setting, particularly when it comes to complicated issues like the integrity of electoral contests. Ferran Martinez i Coma and Carolien Van Ham seek to answer this question, and conclude that expert surveys are useful even when treating complex and multi-faceted issues, such as electoral integrity; and even when carried out in institutional settings as different as liberal democracies and electoral autocracies