| | |
| | |
Stat |
Members: 3645 Articles: 2'506'133 Articles rated: 2609
27 April 2024 |
|
| | | |
|
Article overview
| |
|
Efficient Hopfield pattern recognition on a scale-free neural network | Dietrich Stauffer
; Amnon Aharony
; Luciano da Fontoura Costa
; Joan Adler
; | Date: |
26 Dec 2002 | Subject: | Statistical Mechanics; Neurons and Cognition | cond-mat.stat-mech q-bio.NC | Abstract: | Neural networks are supposed to recognise blurred images (or patterns) of $N$ pixels (bits) each. Application of the network to an initial blurred version of one of $P$ pre-assigned patterns should converge to the correct pattern. In the "standard" Hopfield model, the $N$ "neurons’’ are connected to each other via $N^2$ bonds which contain the information on the stored patterns. Thus computer time and memory in general grow with $N^2$. The Hebb rule assigns synaptic coupling strengths proportional to the overlap of the stored patterns at the two coupled neurons. Here we simulate the Hopfield model on the Barabasi-Albert scale-free network, in which each newly added neuron is connected to only $m$ other neurons, and at the end the number of neurons with $q$ neighbours decays as $1/q^3$. Although the quality of retrieval decreases for small $m$, we find good associative memory for $1 ll m ll N$. Hence, these networks gain a factor $N/m gg 1$ in the computer memory and time. | Source: | arXiv, cond-mat/0212601 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; +claudebot@anthropic.com)
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |