ArticleNature communications2023
Tomography of memory engrams in self-organizing nanowire connectomes.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Real-Time TEM Observation of the Microstructural Evolution in Silver Nanowires under Heating and Electrical Biasing.ACS applied electronic materials · 2026Article
- Memristance and transmemristance in multiterminal memristive systems.Scientific reports · 2026Article
- Ephaptic conduction molding memory engrams.BMC biology · 2025Article
- Self-organizing neuromorphic nanowire networks as stochastic dynamical systems.Nature communications · 2025Article
- Memristive Ion Dynamics to Enable Biorealistic Computing.Chemical reviews · 2025Review
- The precision principle: driving biological self-organization.Frontiers in network physiology · 2025Article
- Brain-like hardware, do we need it?Frontiers in neuroscience · 2024Review
- Online dynamical learning and sequence memory with neuromorphic nanowire networks.Nature communications · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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