SYNCH
A SYnaptically connected brain-silicon Neural Closed-loop Hybrid system
Biomedical
The scientific and technological objective of the project is to create a hybrid system where a neural network in the brain of a living animal and a silicon neural network of spiking neurons on a chip are interconnected by neuromorphic synapses, thus enabling co-evolution of connectivity and co-processing of information of the two networks.
The brain, with its remarkable computational properties, provides animals with capabilities of physical autonomy, interaction and adaptation that are unmatched by any artificial system. The brain is a complex network that has evolved to optimize processing of real-world inputs by relying on electrical events and self-reorganizing connectivity. Spikes (the events) are transmitted between neurons through synapses which undergo continuous ‘birth’-‘death’ and adjustment, reconfiguring brain circuits and adapting processing to ever changing inputs. SYNCH will create a hybrid system where a neural network in the brain of a living animal and a silicon neural network of spiking neurons on a chip are interconnected by neuromorphic synapses, thus enabling co-evolution of connectivity and co- processing of information of the two networks.
SYNCH will provide a better understanding of the computational and adaptive capabilities of brain networks and of autonomous systems based on spiking neurons and brain-inspired synaptic connectivity. The consortium will formulate novel models for homeostatic adaptation, unsupervised feature extraction, and reward-based optimization of SNNs in the context of the hybrid neural system.
EnginSoft will provide its expertise in scientific computing and mathematical modelling and in software development, and it will provide the hardware computing infrastructure, including a 200-core high-performance computing cluster dedicated to simulation and located at its headquarters in Trento. The company will also provide its expertise in the effective use of the following two specific tools: Ansys Maxwell’s code for developing electromagnetic finite element method (FEM) models of neural cells, and Ansys Simplorer’s code for circuit modelling of neural networks. Together with the University of Padua, EnginSoft will develop a biophysical model and simulation software to simulate currents and potentials from spatialtemporal patterns of stimuli generated by an array of capacitive microelectrodes implanted in the brain tissue.
Funding Scheme Horizon2020 | Call identifier H2020-FETPROACT-2018-2020
48 months
January 2020 - December 2023
Università degli studi di Padova
Giovanni Falcitelli
8
by Giovanni Falcitelli | EnginSoft
Futurities - Winter 2023
The SYNCH (SYnaptically connected brainsilicon Neural Closed-loop Hybrid system) Project is a European research project under the Horizon 2020 Future and Emerging Technologies Programme. This innovative effort aims to create a hybrid system in which a neural network in the brain of a living animal interacts with a silicon neural network of spiking neurons via neuromorphic synapses.
The goal of the project is to establish a synapse-inspired reciprocal link between these networks and use the silicon neural network as a processing architecture to adaptively stimulate and rescue functionality in an animal model of disease.
Read the article
Some of our competences in research and technology transfer
Research project
As part of the SMEA project, diagnostic and prognostic methodologies have been studied and implemented, supported by appropriate sensors, for monitoring the functional integrity of a mechanical component typical of the aeronautical and transport sectors.
Research project
The AGILE project arises from the discussions within the Veneto system of companies and research organizations, and from a post-emergency perspective, identifies as its strategic approach the capacity for an "agile" reconversion of production systems through the application of advanced solutions for product innovation. It forms part of the "Smart Manufacturing" specialization strategy.
