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 event-based signaling 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.
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 (a).
We want to provide proof-of-concept demonstration that artificial network on chip, SSN, can act as a "smart" and very-low power consumption device for adaptive electrical brain neuromodulation, by developing a prototype that is validated in the rat brain. As such, the SNN will ‘rescue’ the brain neuronal network, BNN, providing reward signals and treating a learning impairment in a rodent model of psychiatric disease (b).
Overall, we aim at achieving 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. For example, we will formulate novel models for homeostatic adaptation, unsupervised feature extraction, and reward-based optimization of SNNs in the context of the hybrid neural system (c).
Our project represents an invaluable ‘case-study’ and an opportunity to timely investigate ethical implications of technologies based on interaction between brain and artificial intelligence. We will promote an ethical debate involving scientists, technology stakeholders, patients of neurological disorders and clinicians thereby helping to frame a ‘responsible research and innovation’ approach for applications of artificial neural networks in bioengineering, robotics and healthcare (d).