Publication

A Method for All-Positive Optical Multilayer Perceptrons

1996
Conference Papers

Abstract

The most promising approaches for optical neural networks are based on intensity encoding. However, a serious drawback of intensity encoding is the lack of negative values and optical subtraction, which are essential for rendering neural networks useful. To overcome the need for optical subtraction a novel training method is described here that is especially useful for optical multilayer neural networks. In this method, subtraction is implemented as a transformation of the interconnection weights which makes possible the implementation of multilayer perceptrons with optical thresholding. The method is straightforward to implement in optical neural networks where learning occurs under external electronic/computer control.

Official source

https://infoscience.epfl.ch/entities/publication/45c23ec4-b128-4a25-bdf7-92e7e6653d99

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1996
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/45c23ec4-b128-4a25-bdf7-92e7e6653d99

About this result

Related concepts (30)

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Multilayer perceptron

A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable. It is a misnomer because the original perceptron used a Heaviside step function, instead of a nonlinear kind of activation function (used by modern networks).

Physical neural network

A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based approaches. More generally the term is applicable to other artificial neural networks in which a memristor or other electrically adjustable resistance material is used to emulate a neural synapse.

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A Method for All-Positive Optical Multilayer Perceptrons

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