The Self-Trapping Attractor Neural Network-Part II : Properties of A Sparsely Connected Model Storing Multiple Memories  

Oleh:

Pavloski, R. ; Karimi, M.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Neural Networks vol. 16 no. 6 (Nov. 2005), page 1427-1439.
Topik: NEURAL NETWORKS; self - trapping; attractor; neural network; connected model; multiple memories

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II36 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

In a previous paper, the self -t rapping network (STN) was introduced as more biologically realistic than attractor neural networks (ANN s) 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 connectivity. 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 -A NN 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.

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