CCS2016 Day 2 / by Siobhán Cronin

The day was arranged as a rich array of satellite session offerings. 

Talk on the suprachiasmatic nucleus (SCN), "the master clock in the mammalian brain and consists of 20,000 individually oscillating cells". Presented data from mice that showed the interaction of cycles of oscillation during sleep, followed by an immediate tapering off upon waking. Also showed how the cells become desynchronized with age, leading to a decrease of the amplitude of the clock, making it "less strong at driving the [circadian] rhythm". Of note was her nice reminder that the phase differences of individual cells "determine together the sinusoidal oscillations of the the SCN". 

I really enjoyed Christian's rallying cry in his encouragement that we preserve topographical information as a key feature in our analysis. After hearing talks on neural networks that quickly move away from spatial relevance, this reminder was refreshening (as regions of the brain have developed in a spatial reality and offer rich insights into form and function). He focussed on data from retinotopic mapping of the visual system, using mate matching in the connectomic mapping of spectral embedding and estimating multiple modes from the resting rate. 

This mathematician aimed to answer the question, "what is a good network model for brain functionality". In particular, he aimed to reconcile two approaches: 1) inhomogeneous random graphs (rgs), using vertex weights with scale-free behavior (not a spatial model), and 2) instantaneous percolation. In particular he tried to illustrate how the combination of the benefits of these two modes (weights and dependence on space, respectively) could be applied to analyzing the dynamic tradeoffs between learning in pruning in the brain. 

This was an enjoyable exploration of methods, including "Surprise, a recently proposed binary fitness function based on probability theory". Using the surprise quality function you get rid of issues of resolution limit, but there are fewer significant regions. Using asymptotized surprise supports weighted graphs. Referenced neuroSim R package, LFR models, Rician distribution, and Fisher's transformations. 

Discussed multilayer MEG networks, using a layered approach to analyzing mappings of functional and anatomical networks. In this way, function is determined as the weighted sum between all possible random walks through each node. 

Spoke of his work at EGI foundation, including the cloud capabilities of EGI's linking of 300 data centers. He discussed policy initiatives of the EU in four sub-regions of open data: research data, instruments, digital services, and knowledge/expertise. 

Presented an exciting open research lab project in Zaragoza, sponsored by the Fundación Ibercivis. The Etopia Center for Art & Technology is helping non-academic citizens conduct research, and their researchers are winning awards. In particular I was interested to hear the number of participants (in the thousands) they have rallied for crowdsourcing analysis and methods validations initiatives. They are asking the question "what should be a lab in a the 21st century", and the further question of who will have access. 

Overview of La Pallais in Paris, an open wet lab that started as a squat. Great story of how they created an DNA extraction and analysis project to outpace the available meat analysis product on the market (propelled by a public interest in testing horse vs. cow in commercial meat products in France). Compiled their lab from donated materials from other labs, and moved into the center of Paris to begin testing the model more actively. 

This talk has totally captivated my imagination, and I've been dreaming of the consequences of Seranno's work ever since. Her work looks for true cartographic maps by assessing the distance in underlying euclidean spherical space. The shorthand way of grasping her work is to consider the shape of an ocean floor by comparing the "2D" surface image (if you are looking straight down in clear static water) and the actual "3D" topography. How these relate has relevance not only to physical topography, but how we analyze the "topography" of networks. Her work identifies the emergence of hyperbolic geometry in networks.