𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Attractor neural networks and biological reality: associative memory and learning

✍ Scribed by Daniel J Amit


Publisher
Elsevier Science
Year
1990
Tongue
English
Weight
704 KB
Volume
6
Category
Article
ISSN
0167-739X

No coin nor oath required. For personal study only.


πŸ“œ SIMILAR VOLUMES


Gauged neural network: Phase structure,
✍ Motohiro Kemuriyama; Tetsuo Matsui; Kazuhiko Sakakibara πŸ“‚ Article πŸ“… 2005 πŸ› Elsevier Science 🌐 English βš– 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

Associative memory neural networks with
✍ Emanuel Marom πŸ“‚ Article πŸ“… 1990 πŸ› Elsevier Science 🌐 English βš– 498 KB

The efJects oJ vector concatenation as well as that of eliminating the zero diagonal restriction of associative memory neural network matrices is anah, zed. Extensive computer simulations seem to indicate that the t~e of concatenated vectors increases the storage capaci O' of the association matrice

Neural networks as content addressable m
✍ Roland KΓΆberle πŸ“‚ Article πŸ“… 1989 πŸ› Elsevier Science 🌐 English βš– 553 KB

Some apects of content addressable memories implemented by neural networks are discussed, such as storage capacity, non-orthogonal memories, transparency to the outside world, non-equilibrium states, smooth forgetting, etc. As opposed to the usual yon Neumann computers, neural nets are not programme