Materials for neuromorphic circuits
Large efforts are invested into developing computing platforms that will be able to emulate the low power consumption, flexibility of connectivity or programming efficiency of the human brain. The most common approach so far is based on a feedback loop that includes neuroscientists, computer scientists and circuit engineers. Recent successes in this direction motivate the scientific community to start working on the next big challenge: using materials that emulate neural networks. For that, new players are needed: material scientists, who look into alternatives to silicon in order to develop basic device units, more fitting to the needs of cognitive-type processing than current transistors. We notice that recent progress in chemistry and materials sciences (atomically controlled materials) and nanotechnology (diversity of tools to probe the nanometer scale) brings exciting possibilities for novel approaches in the area of neuromorphic computing. Clearly, the type of materials, physical responses and spatial dimensions considered in the design of neuromorphic systems will crucially determine their utilization, properties and cost, and consequently their societal and economic impact. Therefore, it is urgent that chemists and materials scientists also join forces in the development of the future neuromorphic computer. MANIC aims to offer complementary expertise to current approaches by recruiting fifteen Early Stage Researchers (ESRs) and providing them with the best possible research, academic and professional training, to prepare them for the challenge of developing advanced materials with memory, plasticity and self-organization that will perform better than the current solutions to emulate neural networks and, eventually, learn.