Back to Search
Start Over
Enhanced storage capacity with errors in scale-free Hopfield neural networks: An analytical study
- Source :
- PLoS ONE, PLoS ONE, Vol 12, Iss 10, p e0184683 (2017)
- Publication Year :
- 2017
- Publisher :
- Public Library of Science (PLoS), 2017.
-
Abstract
- The Hopfield model is a pioneering neural network model with associative memory retrieval. The analytical solution of the model in mean field limit revealed that memories can be retrieved without any error up to a finite storage capacity of $O(N)$, where $N$ is the system size. Beyond the threshold, they are completely lost. Since the introduction of the Hopfield model, the theory of neural networks has been further developed toward realistic neural networks using analog neurons, spiking neurons, etc. Nevertheless, those advances are based on fully connected networks, which are inconsistent with recent experimental discovery that the number of connections of each neuron seems to be heterogeneous, following a heavy-tailed distribution. Motivated by this observation, we consider the Hopfield model on scale-free networks and obtain a different pattern of associative memory retrieval from that obtained on the fully connected network: the storage capacity becomes tremendously enhanced but with some error in the memory retrieval, which appears as the heterogeneity of the connections is increased. Moreover, the error rates are also obtained on several real neural networks and are indeed similar to that on scale-free model networks.
- Subjects :
- Nerve net
Computer science
lcsh:Medicine
01 natural sciences
Hopfield network
Cognition
Learning and Memory
0302 clinical medicine
Recall (Memory)
Animal Cells
lcsh:Science
Neurons
Multidisciplinary
Artificial neural network
Condensed Matter - Disordered Systems and Neural Networks
Content-addressable memory
medicine.anatomical_structure
Cellular Types
Scale-Free Networks
Algorithm
Network Analysis
Research Article
Computer and Information Sciences
Neural Networks
Scale (ratio)
FOS: Physical sciences
03 medical and health sciences
Memory
Artificial Intelligence
0103 physical sciences
medicine
Phase Diagrams
010306 general physics
Artificial Neural Networks
Computational Neuroscience
Quantitative Biology::Neurons and Cognition
Recall
Data Visualization
lcsh:R
Scale-free network
Biology and Life Sciences
Computational Biology
Disordered Systems and Neural Networks (cond-mat.dis-nn)
Cell Biology
Models, Theoretical
Distribution (mathematics)
Cellular Neuroscience
Cognitive Science
lcsh:Q
Neural Networks, Computer
Neuron
Nerve Net
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....dbc3320bda0daca2a06a014bf3914f08