Understanding the neural basis of mental phenomena remains a great challenge. Statistical physics contributed to the development of attractor neural networks that are our best models linking mental states with the physical properties of the brain. Information (from senses and memory) is embedded in high-dimensional patterns of neural activity. It can be visualized by fMRI scans. Generative...
Memory effects are a ubiquitous feature of nanoscale systems, arising in particular from resistive junctions that give rise to memristive behavior. In this work, we investigate the learning capacity of memristive networks, with a focus on nanowire and nanoparticle architectures. We discuss two examples of learning, e.g., two-phase and contrastive learning with resistive and memristive...
