From Solid to Fluid: Novel Approaches in Neuromorphic Engineering

Neuromorphic engineering is rapidly developing as an approach to mimicking processesin brains using artificial memristors, devices that change conductivity in response to the electricalfield (resistive switching effect). Memristor-based neuromorphic systems can overcome the existingproblems of slow and energy-inefficient computing that conventional processors face. In the Introduction,the basic principles of memristor operation and its applications are given. The history ofswitching in sandwich structures and granular metals is reviewed in the Historical Overview. Particularattention is paid to the fundamental articles from the pre-memristor era (the 1960s-70s), whichdemonstrated the first evidence of resistive switching and predicted the filamentary mechanism ofswitching. Multi-dimensionality in neuromorphic systems: Despite the powerful computationalabilities of traditional memristor arrays, they cannot repeat many organizational characteristics ofbiological neural networks, i.e., their multi-dimensionality. This part reviews the unconventionalnanowire- and nanoparticle-based neuromorphic systems that demonstrate incredible potential foruse in reservoir computing due to the unique spiking change in conductance similar to firing in neurons.Liquid-based neuromorphic devices: The transition of neuromorphic systems from solid to liquidstate broadens the possibilities for mimicking biological processes. In this section, ionic currentmemristors are reviewed and, the working principles of which bring us closer to the mechanisms ofinformation transmittance in real synapses. Nanofluids: A novel direction in neuromorphic engineeringlinked to the application of nanofluids for the formation of reconfigurable nanoparticle networkswith memristive properties is given in this section. The Conclusion t summarizes the bullet points ofthe Review and provides an outlook on the future of liquid-state neuromorphic systems.