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

Identification of functional information subgraphs in cultured neural networks

This paper accompanies an oral presentation on the identification of functional information subgraphs in cultured neural networks

Exploration of hierarchical leadership and connectivity in neural networks in vitro.

Living neural networks are capable of processing information much faster than a modern computer, despite running at significantly lower clock speeds. Therefore, understanding the mechanisms neural networks utilize is an issue of substantial importance. Neuronal interaction dynamics were studied using histiotypic networks growing on microelectrode arrays in vitro. Hierarchical relationships were explored using bursting (when many neurons fire in a short time frame) dynamics, pairwise neuronal activation, and information theoretic measures. Together, these methods reveal that global network activity results from ignition by a small group of burst leader neurons, which form a primary circuit that is responsible for initiating most network-wide burst events. Phase delays between leaders and followers reveal information about the nature of the connection between the two. Physical distance from a burst leader appears to be an important factor in follower response dynamics. Information theory reveals that mutual information between neuronal pairs is also a function of physical distance. Activation relationships in developing networks were studied and plating density was found to play an important role in network connectivity development. These measures provide unique views of network connectivity and hierarchical relationship in vitro which should be included in biologically meaningful models of neural networks

Density-dependence of functional development in spiking cortical networks grown in vitro

During development, the mammalian brain differentiates into specialized regions with distinct functional abilities. While many factors contribute to functional specialization, we explore the effect of neuronal density on the development of neuronal interactions in vitro. Two types of cortical networks, dense and sparse, with 50,000 and 12,000 total cells respectively, are studied. Activation graphs that represent pairwise neuronal interactions are constructed using a competitive first response model. These graphs reveal that, during development in vitro, dense networks form activation connections earlier than sparse networks. Link entropy analysis of dense net- work activation graphs suggests that the majority of connections between electrodes are reciprocal in nature. Information theoretic measures reveal that early functional information interactions (among 3 cells) are synergetic in both dense and sparse networks. However, during later stages of development, previously synergetic relationships become primarily redundant in dense, but not in sparse networks. Large link entropy values in the activation graph are related to the domination of redundant ensembles in late stages of development in dense networks. Results demonstrate differences between dense and sparse networks in terms of informational groups, pairwise relationships, and activation graphs. These differences suggest that variations in cell density may result in different functional specialization of nervous system tissue in vivo.Comment: 10 pages, 7 figure

Spontaneous coordinated activity in cultured networks: analysis of multiple ignition sites, primary circuits, and burst phase delay distributions

This article discusses an analysis of multiple ignition sites, primary circuits, and burst phase delay distributions

Genomic and evolutionary inferences between American and global strains of porcine epidemic diarrhea virus

AbstractPorcine epidemic diarrhea virus (PEDV) has caused severe economic losses both recently in the United States (US) and historically throughout Europe and Asia. Traditionally, analysis of the spike gene has been used to determine phylogenetic relationships between PEDV strains. We determined the complete genomes of 93 PEDV field samples from US swine and analyzed the data in conjunction with complete genome sequences available from GenBank (n=126) to determine the most variable genomic areas. Our results indicate high levels of variation within the ORF1 and spike regions while the C-terminal domains of structural genes were highly conserved. Analysis of the Receptor Binding Domains in the spike gene revealed a limited number of amino acid substitutions in US strains compared to Asian strains. Phylogenetic analysis of the complete genome sequence data revealed high rates of recombination, resulting in differing evolutionary patterns in phylogenies inferred for the spike region versus whole genomes. These finding suggest that significant genetic events outside of the spike region have contributed to the evolution of PEDV