Learning, Memory, and Neural Networks: Introduction
β Scribed by Alan Gelperin
- Book ID
- 125737804
- Publisher
- Marine Biological Laboratory
- Year
- 1996
- Tongue
- English
- Weight
- 108 KB
- Volume
- 191
- Category
- Article
- ISSN
- 0006-3185
- DOI
- 10.2307/1543062
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
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 ΒΌ AE1 on each site x of a 3D lattice and a synaptic-connection variable J xm ΒΌ AE1 on each link Γ°x; x ΓΎ mΓΓ°m ΒΌ 1; 2; 3Γ. The model is regarded
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