Skeleton coupling: a novel interlayer mapping of community evolution in temporal networks

Dynamic community detection (DCD) in temporal networks is a complicated task that involves the selection of an algorithm and its associated parameters. How to choose the most appropriate algorithm generally depends on the type of network being analyzed and the specific properties of the data that define the network. In functional temporal networks derived from neuronal spike train data, communities are expected to be transient, and it is common for the network to contain multiple singleton communities. Here, we compare the performance of different DCD algorithms on functional temporal networks built from synthetic neuronal time series data with known community structure. We find that, for these networks, DCD algorithms that utilize interlayer links to perform community carryover between layers outperform other methods. However, we also observe that algorithm performance is highly dependent on the topology of interlayer links, especially in the presence of singleton and transient communities. We therefore define a novel method for defining interlayer links in temporal networks called skeleton coupling that is specifically designed to enhance the linkage of communities in the network throughout time based on the topological properties of the community history. We show that integrating skeleton coupling with current DCD methods improves algorithm performance in synthetic data with planted singleton and transient communities. The use of skeleton coupling to perform DCD will therefore allow for more accurate and interpretable results of community evolution in real-world neuronal data or in other systems with transient structure and singleton communities.Comment: 19 pages, 8 figure

Functional structure from dynamic clustering of spike train data

http://deepblue.lib.umich.edu/bitstream/2027.42/112959/1/12868_2008_Article_841.pd

Functional clustering in hippocampal cultures: relating network structure and dynamics

In this work we investigate the relationship between gross anatomic structural network properties, neuronal dynamics and the resultant functional structure in dissociated rat hippocampal cultures. Specifically, we studied cultures as they developed under two conditions: the first supporting glial cell growth (high glial group), and the second one inhibiting it (low glial group). We then compared structural network properties and the spatio-temporal activity patterns of the neurons. Differences in dynamics between the two groups could be linked to the impact of the glial network on the neuronal network as the cultures developed. We also implemented a recently developed algorithm called the functional clustering algorithm (FCA) to obtain the resulting functional network structure. We show that this new algorithm is useful for capturing changes in functional network structure as the networks evolve over time. The FCA detects changes in functional structure that are consistent with expected dynamical differences due to the impact of the glial network. Cultures in the high glial group show an increase in global synchronization as the cultures age, while those in the low glial group remain locally synchronized. We additionally use the FCA to quantify the amount of synchronization present in the cultures and show that the total level of synchronization in the high glial group is stronger than in the low glial group. These results indicate an interdependence between the glial and neuronal networks present in dissociated cultures.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85417/1/ph10_4_046004.pd

The Ursinus Weekly, December 8, 1952

Second forum enjoys speech by diplomat • First forum hears Lord • Scribe praises Fall play • 15th annual Messiah performance to be Thursday night in Bomberger chapel • Y plans vespers, town-gown day Christmas party • Senior prom, party is this weekend • Marine speaks to students • Class to hear talk on southeast Asia • Pre-meds to hear Drs. Nye, Eger • Sororities go through informal initiations • Only $510 donated to Campus Chest • U. of P. student addresses IRC • French Club plans party • Editorials: So little time; Prom problem • Color trouble • GOP still split • Letters to the editor • Canterbury Club elects pres., holds meeting tonight at 7 • Weddings • Engagements • Reporter reveals sacred secrets of Weekly hole • Class of \u2753 gives birth to yearbook • Senior ball will feature Stars in the eyes theme • Inquirer relay to be held in January • Bears wallop Pharmacy in season opener, 97-48 • Mermaids begin practice; Sis Bosler is new coach • Kolp elected captain • Dick Glock paces Ursinus offense • Bob Swett tallies 75 as Bears split two decisionshttps://digitalcommons.ursinus.edu/weekly/1508/thumbnail.jp

Coalition for Health and Gender Equity (CHANGE)—a protocol for a global cross-sectional survey of health and gender equity in rheumatology

Lay Summary: What does this mean for patients? The CHANGE Study, led by a team of rheumatology professionals worldwide, is working to make health care more equal for everyone. We are focusing on challenges faced by rheumatologists, such as fair pay and career opportunities. To understand these issues better, the team is gathering information through a global survey of rheumatology professionals. The goal is to find out why there are differences and come up with solutions. Ultimately, the aim is to create a fair and inclusive environment in rheumatology, ensuring that everyone has the same chances to grow in their careers, regardless of their gender. The findings of the study will help to create better guidelines, promoting fairness and equality for health-care professionals in rheumatology

Understanding the Interplay of Structure and Dynamics in Neuronal Networks.

The brain is a prime example of a complex network. The nodes are comprised of neurons and linked through synapses, giving the network an anatomical structure. The fact that neurons communicate with each other through the firing of action potentials allows for the observation of network dynamics, which in turn gives rise to the concept of functional network structure. Here, functional structure refers to groups of neurons that act together to perform a specific function or task. In this dissertation, I address the issue of relating anatomical structure, dynamics, and functional structure in neuronal networks. These three network features are coupled through various dynamic properties which I explore using a variety of methods. I first present a novel algorithm called the Functional Clustering Algorithm which was designed to extract functional groupings from discrete event data. This algorithm provides a method for linking network dynamics with functional structure. I then show applications of the algorithm to experimental and model derived data to explore functional changes as a result of memory consolidation and learning. The application to model derived data allows for a comparison of the dynamics and resulting functional structure with known anatomic structural changes occurring in the model through spike timing dependent plasticity. Next, I explore a system of two coupled networks as a model of focal epilepsy and show that network properties of neurons such as excitability can also drive dynamics, indicating that dynamics and functional structure cannot always be so easily linked to anatomical structure. Finally, I use a reduced system of dissociated hippocampal cultures to simultaneously study the relationship between observed structural differences, network dynamics, and functional structure in cultures with either a high or low density of glial cells.Ph.D.PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/63667/1/sarahfel_1.pd