Horizon 2020 FET-Proactive Project
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. 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 an a chip are interconnected by neuromorphic synapses, thus enabling co-evoultion of connectivity and co-processing of information of the two networks.
Our role in the project: We will develop learning algorithms for spiking neural networks (unsupervised, supervised, and reward-based). Algorithms will first be tested in computer simulations. Hardware experts will then implement these algorithms in cooperation with our team in a spiking neural network chip. The chip will be applied to various tasks within the brain-silicon hybrid system such as the classification of recorded brain network states.
Technological concept for the hybrid system: A neuronal network in the brain of the living animal is contacted by an implantable interface featuring arrays of transducers that can record and stimulate neural signals. Neuronal action potentials are recorded and routed to an artificial spiking neural network (SNN). Biological and SNN signals are further processed by a digital control unit which will be used to stimulate the biological network.
Funded by Austrian Science Fund FWF
Recent experimental results have provided valuable insights in the organization of computations in biological neural networks. In particular, evidence for two main features of cortical computation is rapidly accumulating. First, neurons operate in concert with other neurons in so-called cell assemblies. Second, the activity of single neurons, synapses, and assemblies in the brain is highly stochastic. These findings force us to rethink how computations are organized in cortical neuronal networks. However, an integrated view on stochasticity and assembly organization in spiking neural networks is still missing. In this project, we will investigate stochastic assembly organization both in organic and artificial spiking neural networks. One emphasis will be the characterization of stochastic assembly activation in highly controllable setups and assembly emergence through plasticity processes. Experiments will be accompanied by theoretical modeling, analysis, and computer simulations that will help to understand the basic mechanisms that give rise to assembly formation. In particular, the investigations in this project will focus an (a) the characterization of stochastic assemblies their emergence through plasticity processes in cultured neural networks and acute brain slices, (b) the control of stochastic assemblies in cultured neural networks and its application to neuroprosthetics, and (c) computations in artificial spiking neural networks based on emergent stochastic assemblies with applications to novel computing and learning devices.
Understanding the human brain is one of the greatest scientific challenges of our time. Such an understanding can provide profound insights into our humanity, leading to fundamentally new computing technologies, and transforming the diagnosis and treatment of brain disorders. Modern ICT brings this prospect within reach. The HBP Flagship Initiative (HBP) thus proposes a uniques strategy that uses ICT to integrate neuroscience data from around the world, to develop a unified multi-level understanding of the brain and diseases, and ultimately to emulate its computational capabilities. The goal is to catalyze a global collaberative effort. During the HBP's first Specific Grant Agreement (SGA1), the HBP Core Project will outline the basis for building and operating a tightly integrated Research Infrastructure, providing HBP researchers and the scientific Community with unique resources and capabilities. Partnering projects will enable independent research groups to expand the capabilities of the HBP Platforms, in order to use them to address otherwise intractable problems in neuroscience, computing and medicine in the future. In addition, collaberations with other national, European and international initiatives will create synergies, maximizing returns on research investment.
Funded by Austrian Science Fund FWF
During the past two decades, philosophers, psychologists, cognitive scientists, clinicians and neuroscientists strived to provide authorative definitions of consciousness within a neurobiological framework. Engineers have more recently joined this quest by developing neuromorpic VLSI circuits for emulating biological functions. Yet, to date artificial systems have not been able to faithfully recreate natural attributes such as true processing locality (memory anf computation) and complexity (1010 synapses per cm2), preventing the achievment of a long-term goal: the creation of autonomous cognitive systems. This project aspires to develop experimental platforms capable of perceiving, learning and adapting to stimuli by leveraging on the latest developments of five leading European institutions in neuroscience, nanotechnology, modeling and circuit design. The non-linear dynamics as well as the plasticity of the newly discovered meristor are shown to support Spike-based- and Spike-Timing-Dependent-Plasticity (STDP), making this extremely compact device an excellent candidate for realizing large-scale self-adaptive circuits; a step towards autonomous cognitive systems. The intrinsic properties of real neurons and synapses as well as their organization in forming neural circuits will be exploited for optimizing CMOS-based neurons, memristive grids and the integration of the two into realtime biophysically realistic neuromorphic systems. Finally, the platforms would be tested with conventional as well as abstract methods to evaluate the technology and its autonomous capacity.