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Unveiling the relationship between brain connectivity and function by integrated photonics

Total Cost €


EC-Contrib. €






Project "BACKUP" data sheet

The following table provides information about the project.


Organization address
address: VIA CALEPINA 14
city: TRENTO
postcode: 38122

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 2˙499˙825 €
 EC max contribution 2˙499˙825 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-ADG
 Funding Scheme ERC-ADG
 Starting year 2018
 Duration (year-month-day) from 2018-11-01   to  2023-10-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    UNIVERSITA DEGLI STUDI DI TRENTO IT (TRENTO) coordinator 2˙499˙825.00


 Project objective

I will address the fundamental question of which is the role of neuron activity and plasticity in information elaboration and storage in the brain. I, together with an interdisciplinary team, will develop a hybrid neuro-morphic computing platform. Integrated photonic circuits will be interfaced to both electronic circuits and neuronal circuits (in vitro experiments) to emulate brain functions and develop schemes able to supplement (backup) neuronal functions. The photonic network is based on massive reconfigurable matrices of nonlinear nodes formed by microring resonators, which enter in regime of self-pulsing and chaos by positive optical feedback. These networks resemble human brain. I will push this analogy further by interfacing the photonic network with neurons making hybrid network. By using optogenetics, I will control the synaptic strengthen-ing and the neuron activity. Deep learning algorithms will model the biological network functionality, initial-ly within a separate artificial network and, then, in an integrated hybrid artificial-biological network. My project aims at: 1. Developing a photonic integrated reservoir-computing network (RCN); 2. Developing dynamic memories in photonic integrated circuits using RCN; 3. Developing hybrid interfaces between a neuronal network and a photonic integrated circuit; 4. Developing a hybrid electronic, photonic and biological network that computes jointly; 5. Addressing neuronal network activity by photonic RCN to simulate in vitro memory storage and retrieval; 6. Elaborating the signal from RCN and neuronal circuits in order to cope with plastic changes in pathologi-cal brain conditions such as amnesia and epilepsy. The long-term vision is that hybrid neuromorphic photonic networks will (a) clarify the way brain thinks, (b) compute beyond von Neumann, and (c) control and supplement specific neuronal functions.

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