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Maurizio De Pittà, the researcher.
Hugues Berry, the scientist in charge at INRIA (return institution).
Nicolas Brunel, the scientist in charge at The University of Chicago (outgoing host institution).

The main aim of the project PIOF-GA-2012-331486 “Neuron-Astro-Nets” was to seek theoretical understanding of the mechanisms that control and regulate the activity of mixed neuron-glial networks with emphasis on the regulation of synaptic transmission by astrocytes – the most numerous glial cells in the brain. To unravel the complexity of neuron-astrocyte interactions, the project pursued a bottom-up approach to characterize the dynamics of neuron-glia networks on different scales of investigation. Specifically, the project sought theoretical characterization of: (1) individual synapses modulated by an astrocyte; (2) synaptic ensembles within the same astrocytic domain; and (3) networks of coupled neurons and astrocytes whereby synaptic connections between neurons are spatially and temporally modulated by astrocytes. Upon completion of the project, all objectives have successfully been pursued.

The project consisted of a 2-year-long outgoing phase at the University of Chicago at the Dr. Nicolas Brunel’s group in computational neuroscience, and of a 1-year-long return phase at INRIA Rhône-Alpes at the BEAGLE Team in artificial cell biology, under the supervision of Dr. Hugues Berry. The overall research goals foresaw:(i) the development of a biophysical modeling framework to simulate and study the effect of astrocyte-mediated modulation of synaptic transmission on synaptic plasticity (objectives 1 and 2); and (ii) developing the core theory of neuron-glia networks in an analytically tractable model of such networks (objective 3). Of these two goals, only the first one has been published by the time of this report. The mathematical theory of neuron-glia networks, although completed by the end of the project has only been presented at international conferences and several scientific venues so far but its effective publication is currently pending.

The project’s training objectives focused on the acquisition by the researcher of analytical techniques for analysis of complex, heterogeneous networks such as neuron-glia networks, and the identification and use of mathematical tools suitable for the formalization of new rules for learning and retrieval of information mediated by neuron-glia signaling. By the end of the project Dr. De Pittà’s international recognition has consistently grown, and the researcher is regarded by his peers and senior investigators as one of the prominent young European investigators in computational research on glia modeling. During the project, Dr. De Pittà’s h-index increased from 6 to 11 (source: Google Scholar).

In modern neuroscience, as well as for the R&D community, where interests on machine learning and complex network theory have steadily grown for the past two decades, the novel quantitative understanding of neuronal dynamics and learning mediated by glia ensued by this project is expected to spur interest on the subject towards new generations algorithms for fast and efficient learning and data extraction. At the same time, the identification of specific conditions for abnormal neuronal activity mediated by glia provides the project’s results with translation value, insofar as persistent, high-rate neuronal firing is a common feature of neuronal pathologies.