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The Self-Trapping Attractor Neural Network— Part II: Properties of a Sparsely Connected Model Storing Multiple Memories
- Source :
- IEEE Transactions on Neural Networks. 16:1427-1439
- Publication Year :
- 2005
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2005.
-
Abstract
- In a previous paper, the self-trapping network (STN) was introduced as more biologically realistic than attractor neural networks (ANNs) based on the Ising model. This paper extends the previous analysis of a one-dimensional (1-D) STN storing a single memory to a model that stores multiple memories and that possesses generalized sparse connnectivity. The energy, Lyapunov function, and partition function derived for the 1-D model are generalized to the case of an attractor network with only near-neighbor synapses, coupled to a system that computes memory overlaps. Simulations reveal that 1) the STN dramatically reduces intra-ANN connectivity without severly affecting the size of basins of attraction, with fast self-trapping able to sustain attractors even in the absence of intra-ANN synapses; 2) the basins of attraction can be controlled by a single free parameter, providing natural attention-like effects; 3) the same parameter determines the memory capacity of the network, and the latter is much less dependent than a standard ANN on the noise level of the system; 4) the STN serves as a useful memory for some correlated memory patterns for which the standard ANN totally fails; 5) the STN can store a large number of sparse patterns; and 6) a Monte Carlo procedure, a competitive neural network, and binary neurons with thresholds can be used to induce self-trapping.
- Subjects :
- Lyapunov function
Theoretical computer science
Computer Networks and Communications
Computer science
Information Storage and Retrieval
Topology
Synapse
symbols.namesake
Biomimetics
Memory
Artificial Intelligence
Attractor
Content-addressable storage
Computer Simulation
Attractor network
Quantitative Biology::Neurons and Cognition
Artificial neural network
Signal Processing, Computer-Assisted
General Medicine
Models, Theoretical
Content-addressable memory
Computer Science Applications
symbols
Neural Networks, Computer
Algorithms
Software
Attractor neural network
Subjects
Details
- ISSN :
- 10459227
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks
- Accession number :
- edsair.doi.dedup.....b719ebb76d867eb321a833653f478ea5