Memristor-based neural networks

By Andy Thomas. 2013

444 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/597762f1f1584bf0de78a45df9cc2d30f490c2f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Seiji Takeda
2. Gouhei Tanaka
3. Naoki Kanazawa
4. Daiju Nakano
5. Toshiyuki Yamane
6. Akira Hirose
7. Ryosho Nakane
8. Hidetoshi Numata

In Electrical and Electronic Engineering

1. Seiji Takeda
2. Gouhei Tanaka
3. Naoki Kanazawa
4. Daiju Nakano
5. Toshiyuki Yamane
6. Akira Hirose
7. Ryosho Nakane
8. Hidetoshi Numata

Citations in the corpus

• Recent Advances in Physical Reservoir Computing: A Review (2018, background): “…systems are a subclass of neuromorphic computing systems that use mem-ristors to mimic the synaptic plasticity in biological neurons, where the memristor conductance corresponds to the synaptic weight (Indiveri, Linares-Barranco, Legenstein, Deligeorgis, & Pro-dromakis, 2013; Thomas, 2013).”