✦ LIBER ✦

Associative search network: A reinforcement learning associative memory

✍ Scribed by Andrew G. Barto; Richard S. Sutton; Peter S. Brouwer

Publisher: Springer-Verlag
Year: 1981
Tongue: English
Weight: 991 KB
Volume: 40
Category: Article
ISSN: 0340-1200
DOI: 10.1007/bf00453370

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Digital associative memory neural networ

Digital associative memory neural network with optical learning capability

✍ Minoru Watanabe; Junji Ohtsubo 📂 Article 📅 1994 🏛 Elsevier Science 🌐 English ⚖ 719 KB

Gauged neural network: Phase structure,

Gauged neural network: Phase structure, learning, and associative memory

✍ Motohiro Kemuriyama; Tetsuo Matsui; Kazuhiko Sakakibara 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 579 KB

A gauge model of neural network is introduced, which resembles the Z(2) Higgs lattice gauge theory of high-energy physics. It contains a neuron variable S x ¼ AE1 on each site x of a 3D lattice and a synaptic-connection variable J xm ¼ AE1 on each link ðx; x þ mÞðm ¼ 1; 2; 3Þ. The model is regarded

Adaptive quick learning for associative

Adaptive quick learning for associative memory

✍ Tomoshige Yoshihara; Masafumi Hagiwara 📂 Article 📅 2000 🏛 John Wiley and Sons 🌐 English ⚖ 184 KB

Bidirectional associative memory (BAM) is a a form of heteroassociative memory that can recall and restore patterns. Being a Hebbian learning-based memory, it has the problem of very low capacity. A training paradigm called the Pseudo-Relaxation Learning Algorithm (PRLAB) greatly increases the memor

Landmark learning: An illustration of as

Landmark learning: An illustration of associative search

✍ Andrew G. Barto; Richard S. Sutton 📂 Article 📅 1981 🏛 Springer-Verlag 🌐 English ⚖ 736 KB

The correlational associative memory rea

The correlational associative memory realizes Hebbian learning

✍ N.B. Karayiannis; A.N. Venetsanopoulos 📂 Article 📅 1988 🏛 Elsevier Science 🌐 English ⚖ 64 KB

CAM) was originally proposed as an ad hoc scheme in the early seventies. In the early eighties, the validity of this type of associative memory was justified on the basis of rather intuitive arguments and using asynchronous network dynamics. Since then, a series of interesting new results on the sub

Associative learning in a network model

Associative learning in a network model ofHermissenda crassicornis

✍ Susan A. Werness; S. Dale Fay; Kim T. Blackwell; Thomas P. Vogl; Daniel L. Alkon 📂 Article 📅 1992 🏛 Springer-Verlag 🌐 English ⚖ 909 KB

A time-varying Resistance-Capacitance (RC) circuit computer model was constructed based on known membrane and synaptic properties of the visualvestibular network of the marine snail Hermissenda crassicornis. Specific biophysical properties and synaptic connections of identified neurons are represent