Tomography of memory engrams in self-organizing nanowire connectomes

Self-organizing memristive nanowire connectomes have been exploited for physical ( in materia ) implementation of brain-inspired computing paradigms. Despite having been shown that the emergent behavior relies on weight plasticity at single junction/synapse level and on wiring plasticity involving topological changes, a shift to multiterminal paradigms is needed to unveil dynamics at the network level. Here, we report on tomographical evidence of memory engrams (or memory traces) in nanowire connectomes, i.e., physicochemical changes in biological neural substrates supposed to endow the representation of experience stored in the brain. An experimental/modeling approach shows that spatially correlated short-term plasticity effects can turn into long-lasting engram memory patterns inherently related to network topology inhomogeneities. The ability to exploit both encoding and consolidation of information on the same physical substrate would open radically new perspectives for in materia computing, while offering to neuroscientists an alternative platform to understand the role of memory in learning and knowledge. Hardware architectures based on self-organized memristive networks of nano objects have attracted a growing attention. Here, nanowire connectomes are experimentally proved to translate spatially correlated short-term plasticity effects into long-lasting topological changes, thus emulating both information encoding and memory consolidation of human brain.