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Alessandro Scaglione


Alessandro Scaglione is a researcher in the neurophotonics group of dr. Francesco Pavone at LENS. His main interest lies in neuroscience especially in the study of neural circuits underlying behavior. For this he has developed computational tools to study how information is encoded in the brains from single to network of networks of neurons in awake and behaving animals. In recent years he is interested in finding neural correlates of social interactions in the awake freely moving animals. To this aim he is developing miniaturized tools to monitor neural activity using lights in awake freely moving animals. The main objective is to develop a model of social interaction that can recapitulate the findings that are found in human studies in hyperscanning studies. For this reason, he is also actively involved in the human sensing group to study the effect of social interaction in humans and how this modulates higher cognitive functions and how this correlates with other biosignals in from the autonomic nervous system. Alessandro has a Ph.D. in biomedical engineering from Drexel University, Philadelphia PA where he learned extracellular recordings in awake freely moving animals and developed a theoretical framework based on Shannon mutual information to quantify and separate the amount of information carried by neurons in response to somatosensory stimuli. He subsequently worked at the National Institute of Health where he learned optogenetics in awake freely moving animals in a go/no go task where he developed a framework to identify basal forebrain neurons related to salience. Since 2018 he has joined the neurophotonics group at LENS where he has learned neuroimaging methods to monitor neural activity over almost the entire cortical mantle in awake behaving and/or freely moving animals. He has developed computational tools and miniaturized devices to analyze the neuroimaging data. He is also actively collaborating with other members of the neurophotonics group to provide aid in data analysis and modeling.