The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

We designed and built a high-capacity neural network based on volume holographic interconnections in a photorefractive crystal. We used this system to implement a Kohonen topological map. We describe and justify our optical setup and present some experimental results of self-organization in the learning database.

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High-capacity photorefractive neural network implementing a kohonen topological map.

We have constructed an optical neural-network system with learning
capability by using a Pockels readout optical modulator. The system
has a two-dimensional structure that permits easy optical alignment and
can handle images without scanning. Learning signals are calculated
optically with two liquid-crystal devices by a matrix–matrix
outer-product method. The calculated learning signals are added
directly to the weights memorized on the Pockels readout optical
modulator. A two-layer network is implemented, and the learning and
recognition of four alphabetical characters are realized according to
the delta rule.

Optical learning neural network with a Pockels readout optical modulator

Topological map from a photorefractive self-organizing neural network

Optically addressed ferroelectric liquid-crystal devices present certain characteristics that make them attractive for all-optical implementations of neural architectures. This is particularly true for the implementation of analog self-organizing systems such as Kohonen's self-organizing map. The optical architecture proposed here is based on the principle of a resonator memory. The main novelty lies in the two-dimensional, continuous, binary information encoding and organization. Basic operations such as neural decision, weight updating, and information spatial ordering are investigated. The principle of a learning procedure close to the Hebbian rule is discussed.

All-optical implementation of a self-organizing map: a preliminary approach.

We present some experimental results obtained with an all-optical implementation of a self-organizing map. The system is based on two ferroelectric liquid-crystal, bistable optically addressed spatial light modulators (BOASLM's) used as parallel read/write optical memories and as thresholding devices. Modifications made to adapt the basic algorithm to the BOASLM's are studied in computer simulations that predict training-class loss due to the simplified decision stage as the principal drawback. Experimental results show good system behavior in recognition mode, demonstrating generalization properties and confirming the choice of a BOASLM memory. All-optical learning is shown in simple cases, and the elementary BOASLM decision function is confirmed as the major limiting factor. The ultimate possible system performance is discussed, and ways of overcoming present limits are suggested.

J. Duvillier

Papers

All-optical implementation of a self-organizing map: a preliminary approach.

All-optical implementation of a self-organizing map: Learning and taxonomy capability assessment.