Lifeboat Foundation

Networks (connectivity) and dynamics are two key pillars of network neuroscience – an emerging field dedicated to understanding structure and function of neural systems across scales, from neurons to circuits to the whole brain. In this presentation I will review current themes and future directions, including structure/function relationships, use of computational models to map information flow and communication dynamics, and a novel edge-centric approach to map functional connectivity at fine temporal scales. I will argue that network neuroscience represents a promising theoretical framework for understanding the complex structure and functioning of nervous systems.

This video is part of the SNAC seminar series organized by Mac Shine, Joe Lizier, and Ben Fulcher (The University of Sydney).

A team of researchers led by Yale University.

Established in 1,701, Yale University is a private Ivy League research university in New Haven, Connecticut. It is the third-oldest institution of higher education in the United States and is organized into fourteen constituent schools: the original undergraduate college, the Yale Graduate School of Arts and Sciences and twelve professional schools. It is named after British East India Company governor Elihu Yale.

“This new study joins a growing body of scientific evidence that raises questions about the irreversible nature of death…”

Scientists have revived light-sensing neuron cells in organ donor eyes and restored communication between them as part of a series of discoveries that could transform research into the brain-vision system.

Can we deploy a constellation of solar power generating satellites to energize the entire planet? It is feasible.

In this second part of a two-part series, we look at a constellation of solar satellites as an alternative to geosynchronous power arrays.

The robotic explorer GLIMPSE, created at ETH Zurich and the University of Zurich, has made it into the final round of a competition for prospecting resources in space. The long-term goal is for the robot to explore the south polar region of the moon.

The south polar region of the moon is believed to contain many resources that would be useful for lunar base operations, such as metals, water in the form of ice, and oxygen stored in rocks. But to find them, an explorer robot that can withstand the extreme conditions of this part of the moon is needed. Numerous craters make moving around difficult, while the low angle of the sunlight and thick layers of dust impede the use of light-based measuring instruments. Strong fluctuations in temperature pose a further challenge.

The European Space Agency (ESA) and the European Space Resources Innovation Center ESRIC called on European and Canadian engineering teams to develop robots and tools capable of mapping and prospecting the shadowy south polar region of the moon, between the Shoemaker and the Faustini craters. To do this, the researchers had to adapt terrestrial exploration technologies for the harsh conditions on the moon.

Deep learning models have proved to be highly promising tools for analyzing large numbers of images. Over the past decade or so, they have thus been introduced in a variety of settings, including research laboratories.

In the field of biology, deep learning models could potentially facilitate the quantitative analysis of microscopy images, allowing researchers to extract meaningful information from these images and interpret their observations. Training models to do this, however, can be very challenging, as it often requires the extraction of features (i.e., number of cells, area of cells, etc.) from microscopy images and the manual annotation of training data.

Researchers at CERVO Brain Research Center, the Institute for Intelligence and Data, and Université Laval in Canada have recently developed an artificial neural network that could perform in-depth analyses of microscopy images using simpler, image-level annotations. This model, dubbed MICRA-Net (MICRoscopy Analysis neural network), was introduced in a paper published in Nature Machine Intelligence.