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Unveiling the relationship between brain connectivity and function by integrated photonics

Postdoc position in “Biological neural-network signal encoding using Artificial Intelligence”

The goal of this project is to propose and develop a general methodology for biological neuronal network signal encoding. Specifically, we will use a Multi-Electrode Array (MEA) to access the activity of a culture of in-vitro alive neurons. The MEA response is a set of time-varying signals, one signal per electrode. Because of the weak currents, the signals have a very low Signal-to-Noise-Ratio (SNR). These raw signals will be used to train an artificial neural network in order to predict (“encode”) the biological neuron activity. Particularly challenging is to automatically extract significant features of the biological neuronal network behaviour from the noisy signals. The project will investigate Deep Learning methods for brain (neuronal network) activity encoding and will generalize the proposed methodology to different kinds of brain activity signals (e.g., fMRI, MEG, etc.).

For more info please contact at enver.sangineto@unitn.it and read here 

Two fellowhisps are offered for PhD positions on BACKUP topics

Neuromorphic silicon photonics (PhD program in Physics)

Within the ERC project BACKUP, we aim investigate a Photonic Extreme Learning Machine (PELM) which is an evolution of the so-called Reservoir Computing Network (RCN) paradigm. PELM is characterized by the easiness of training, which makes PELM quite feasible in silicon photonics. The aim of the PhD is to implement PELMs using Silicon photonics to understand the Si-PELM from the basic rules to the design of the optimal network and to boost its performances towards features extraction rather than simple classification problems.

Application should be submitted here

Photonics for brain circuit physiology (PhD program in biomolecular sciences)

Within the ERC project BACKUP, we aim at identifying the basic principles governing neural engram: a group of neurons that can be recruited together as a consequence of a learning process. We will develop an Integrated Photonic-neuromorphic-computing Platform for coding, consolidation, storage and retrieval of a memory trace created in vitro. Molecular assessments of artificial neural engram will further provide relevant mechanistic information about the significance and mode of action of a memory process.

Application should be submitted here

Photonic Reservoir Computing and information processing in complex network

We are pleased to announce that from 4th to 6th of December 2019 in Trento (Italy) we are organizing a workshop on “Photonic Reservoir Computing and information processing in complex network” at the premises of the Bruno Kessler Foundation.

The workshop is organized within the ERC AdG project Backup “Unveiling the relationship between brain connectivity and function by integrated photonics” and the PRIN project PELM “Photonic Extreme Learning Machine: from neuromorphic computing to universal optical interpolant, strain gauge sensor and cancer morphodynamic monitor”.

For more info please visit the event site 

Mission

BACKUP will address the fundamental question of which is the role of neuron activity and plasticity in information elaboration and storage in the brain. Within an interdisciplinary team, BACKUP will develop a hybrid neuromorphic 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. BACKUP will push this analogy further by interfacing the photonic network with neurons making hybrid network. By using optogenetics, we will control the synaptic strengthening and the neuron activity. Deep learning algorithms will model the biological network functionality, initially within a separate artificial network and, then, in an integrated hybrid artificial-biological network.

  • Developing a photonic integrated reservoir-computing network (RCN);
  • Developing dynamic memories in photonic integrated circuits using RCN;
  • Developing hybrid interfaces between a neuronal network and a photonic integrated circuit;
  • Developing a hybrid electronic, photonic and biological network that computes jointly;
  • Addressing neuronal network activity by photonic RCN to simulate in vitro memory storage and retrieval;
  • Elaborating the signal from RCN and neuronal circuits in order to cope with plastic changes in pathological brain conditions such as amnesia and epilepsy.

Long term vision: The long-term vision is that hybrid neuromorphic photonic networks will

  1. clarify the way brain thinks
  2. compute beyond von Neumann
  3. control and supplement specific neuronal functions

This project has a strong interdisciplinary content. We primarily address computing, photonics, electronics and photonics integrated circuits, photonic applications to biology and networks. However, we do also address the issue of interfacing neurons with condensed matter by using light, which is a topic peculiar to biophysics. In this research, we will develop photonic circuits that provide the light signal to genetically modified neurons in order to control their activity. Moreover, our intention is to use light in order to both strengthen synapses along specific light circuits and to restore or induce specific neuron interconnections, to achieve specific neuronal functions. BACKUP results will be applied to predict and control the mechanisms behind complex neurological diseases as amnesia and epilepsy.