Plasticity in NEUral Memristive Architectures

During the past two decades, philosophers, psychologists, cognitive scientists, clinicians and neuroscientists strived to provide authoritative definitions of consciousness within a neurobiological framework. Engineers have more recently joined this quest by developing neuromorphic 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 and computation) and complexity (10^10 synapses per cm2), preventing the achievement 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 memristor 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 optimising 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.

The work proposed here will provide answers to an extremely diverse audience including: computer science, electronics, physics, neuroscience, medicine, education and many other communities that are intrigued by the way the human brain functions. Thus the anticipated impact will expand across three main areas:
1. Economy
2. Host Institutions (CBiT, IMSE, INI, IGI and LAAS), EU science-base
3. Society
Economic Impact: The main beneficiaries stand to be the semiconductor and micro/nanoelectronics industries. The proposed research promotes the use of memristors in complex networks that could be independently trained to solve tasks in which conventional supercomputers prove to be inadequate. The large impact of this newly discovered device is expected to be reflected in the semiconductor industry by adjusting processing techniques and materials to accommodate the fabrication of memristors along with conventional devices in standard technologies.
Memristive networks could potentially be mingled with existing processing architectures, as extra processing cores, for carrying out assignments in which perception, reasoning and motivation are required. We therefore anticipate that this research will have a significant impact in the computer-science industry, since this technological advancement will reshape the current computation strategies and it will eventually act as the enabler to new applications. Particular benefits are thus foreseen in leading companies that impose the designs of novel processing unit architectures like IBM, Intel and AMD. HP has already invested a lot in the development of memristors and memristive networks, demonstrating existing potential for future emerging technologies.
Although, digital computers are good in number crunching, they struggle with tasks like face recognition, real-time navigation control, object segmentation and depth perception. Therefore, it is not unrealistic to say that this project is a step towards the development of autonomous systems, with all the apparent benefits for the robotics industry.
An added benefit of the proposed technology is its extreme versatility, since the processing of different impulses is contingent on the training of the system. Memristive networks could also be applied to solve mathematical problems that do not formulate a specific algorithmic solution, with particular applicability in modeling stock trading. Last but not least, the proposed technology could be utilized to demonstrate associative indexing of information, which is particularly useful in large database architectures and the Internet for increasing the speed of information retrieval.

Impact to the Host Organisations and to the European Union: See Academic Beneficiaries.

Impact to Society: Biology makes excellent use of resources to solve a given task, being efficient, robust, adaptable, real-time, effective, scalable and reliable. For the above reasons,
any engineer would do extremely well in learning from nature. The experimental platforms we propose to develop would shine more light in the way the human brain, and biology in
general, learns and adapts according to its environment. This question has often been addressed by a broad range of disciplines, from philosophy to medicine and more recently
engineering. Thereby, one can appreciate how important it is to have the physical means of studying and understanding how knowledge evolves.
Apart from providing knowledge to society, we believe that this project could be further exploited, leading into advances on international development, primarily in healthcare. Without any doubt, stimulating patterns for treating epilepsy and other brain-related diseases could be safely tested on benchmarking memristive platforms, before initiating clinical trials. Finally, the "quality of life" enhancement is highlighted by the employment of synaptic-like architectures both in healthcare applications as well as automation, for example cruise control