Maurizio De Pitta
PhD, Tel Aviv University
At A Glance
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We pioneered artificial spiking neuron-astrocyte networks to characterize manifold geometry and dynamics of the neuron-glial interactome in memory processing and age-related dementias.
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We develop generative algorithms to reconstruct the neuron-glial connectome from microscopy datasets.
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We deploy multiscale modeling to bridge gene data with functional readouts, in an attempt to simulate whole-cell signaling.
Short Bio
Dr. Maurizio De Pitta is a computational neuroscientist at the Krembil Research Institute and Principal Investigator of the Krembil Computational Neuroscience Hub. He earned his PhD from Tel Aviv University (Israel) and completed postdoctoral training under Nicolas Brunel at the University of Chicago (USA) as a Marie Sklodowska-Curie Fellow. He established his first independent research group, funded by one of the prestigious Junior Leader Fellowships from la Caixa Foundation, at the Basque Center for Applied Mathematics in Bilbao, Spain, before relocating to Canada in 2021.
Dr. De Pitta's research combines computational modeling, synthetic biology, machine learning, and translational neuroscience with the overarching aim of characterizing how neuron-glial cell interactions contribute to brain function in health and disease. A pioneer in computational glioscience, Dr. De Pitta is the lead author of the first graduate monograph dedicated to this discipline and an advocate for expanding theories of cognition to include the role of glial cells. His work offers critical insights into the mechanisms of neurological disorders and supports the development of novel diagnostics and therapeutic approaches that resolve neuron-glial interactions from genome to behavioral levels.
Research Synopsis
Multiscale Modeling of the Neuron-Glial Interactome. We develop computational frameworks to investigate neuron-glial interactions across scales—from genome dynamics to circuit activity and behavior—aiming to elucidate their roles in brain function and dysfunction.
Interdisciplinary and Quantitative Approach. Our team brings together expertise in physics, applied mathematics, computer science, and engineering to integrate biophysical modeling, quantitative physiology, and bioinformatics in a systems-level understanding of brain physiology.
Translational and AI-Driven Innovation. By decoding the computational logic of the neuron-glial interactome, we aim to uncover novel diagnostic and therapeutic targets for neurological disorders, while also drawing inspiration from brain physiology to inform next-generation machine learning and AI algorithms.
Recent Publications
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Rueda-Alaña, Senovilla-Ganzo, Grillo, Vázquez, Marco-Salas, Gallego-Flores, Ordeñana Manso, Ftara, Escobar, Benguría, Quintas, Dopazo, Rábano, de Vivanco, Aransay, Garrigos, Toval, Ferrán, Nilsson, Encinas, De Pittà, and García-Moreno. Evolutionary convergence of sensory circuits in the pallium of amniotes. Science 387.6735 (2025): eadp3411.
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Bonifazi, Luchena, Gaminde-Blasco, Ortiz-Sanz, Capetillo-Zarate, Matute, Alberdi, and De Pittà. A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity. Neurobiology of Disease, 2024:106473.
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Moradi Chameh, Falby, Movahed, Arbabi, Rich, Zhang, Lefebvre, Tripathy, De Pittà, and Valiante. Distinctive biophysical features of human pyramidal cell types: Insights from studies of neurosurgically resected brain tissue. Frontiers Synaptic Neuroscience, 2023:15:1250834.
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De Pittà, and Brunel, Multiple forms of working memory emerge from synapse–astrocyte interactions in a neuron–glia network model. Proceedings Nat'l Academy Science USA, 2022:119 (43) e2207912119.
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De Pittà, and Berry, Computational Glioscience, Springer 2019.
Graduate Students
Zhenyang Sun
Michelangelo Volpi