The successful candidate will work on the Synapsis-foundation funded research project “Real-life activity tracking as pre-screening tool for early stages of Alzheimer disease”. The aim of the project is to investigate whether real-life activity measures, derived from wearable technology (e.g. GPS and accelerometer data), are sensitive to identify early stages of Alzheimer’s disease. Further, we aim to provide evidence that these real-life activity measures are associated with current AD biomarkers (i.e. high Amyloid level and brain atrophy). The student will be expected to disseminate study results in peer reviewed journals, and to supervise Master’s students. The candidate will work in the team of Prof. Nicolas Langer, who is also part of the Neuroscience Center Zurich (ZNZ) (https://www.neuroscience.uzh.ch/en.html), which offers a renowned international PhD programme in Neuroscience. The candidate will work closely with the Institute for Regenerative Medicine (https://www.irem.uzh.ch/en.html), Geographic Information Systems (https://www.geo.uzh.ch/en/units/gis.html), University Research Priority Programme from the University of Zurich “Dynamics of Healthy Aging” (https://www.dynage.uzh.ch/en.html), and the Department of Computer Science at the ETH Zurich (https://www.systems.ethz.ch/). For further information, please visit: https://tinyurl.com/yvyjkvv9

The project is related to a collaboration between the University of Naples Parthenope and the National Institute of Geophysics and Volcanology (INGV) for the understanding of natural hazards deriving from the study of geophysical data, such as those related to the Phlegraean fields in the Naples area, by means of Artificial Intelligence-based techniques. The aim is to study, develop and apply Artificial Intelligence methodologies, such as Deep Learning and Neuro-Symbolic techniques, to multimodal geophysical data (e.g., localized GPS and seismic signals for the study of ground deformation) for environmental risk assessment with a focus on context awareness and eXplainable Artificial Intelligence.

Humans share with other animals a complex neuronal machinery that evolved to support navigation in the physical space and that supports wayfinding and path integration. In my talk I will present a series of recent neuroimaging studies in humans performed in my Lab aimed at investigating the idea that this same neural navigation system (the “brain GPS”) is also used to organize and navigate concepts and memories, and that abstract and spatial representations rely on a common neural fabric. I will argue that this might represent a novel example of “cortical recycling”, where the neuronal machinery that primarily evolved, in lower level animals, to represent relationships between spatial locations and navigate space, in humans are reused to encode relationships between concepts in an internal abstract representational space of meaning.

Bats are extreme aviators and amazing navigators. Many bat species nightly commute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has always been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based navigation. In a second study, we examined how nectar-feeding bats make foraging decisions under competition. We show that by relying on a simple reinforcement learning strategy, the bats can divide the resource between them without aggression or communication. Together, these results demonstrate the power of the large scale natural approach for studying animal behavior.

Bats are extreme aviators and amazing navigators. Many bat species nightly com-mute dozens of kilometres in search of food, and some bat species annually migrate over thousands of kilometres. Studying bats in their natural environment has al-ways been extremely challenging because of their small size (mostly <50 gr) and agile nature. We have recently developed novel miniature technology allowing us to GPS-tag small bats, thus opening a new window to document their behaviour in the wild. We have used this technology to track fruit-bats pups over 5 months from birth to adulthood. Following the bats’ full movement history allowed us to show that they use novel short-cuts which are typical for cognitive-map based naviga-tion. In a second study, we examined how nectar-feeding bats make foraging deci-sions under competition. We show that by relying on a simple reinforcement learn-ing strategy, the bats can divide the resource between them without aggression or communication. Together, these results demonstrate the power of the large scale natural approach for studying animal behavior.