Publication

Online processing of multiple inputs in a sparsely-connected recurrent neural network

Wulfram Gerstner, Julien Mayor
2003
Conference Papers

Abstract

The storage and short-term memory capacities of recurrent neural networks of spiking neurons are investigated. We demonstrate that it is possible to process online many superimposed streams of input. This is despite the fact that the stored information is spread throughout the network. We show that simple output structures are powerful enough to extract the diffuse information from the network. The dimensional blow up, which is crucial in kernel methods, is efficiently achieved by the dynamics of the network itself.

Official source

https://infoscience.epfl.ch/entities/publication/cdaee27b-4564-4999-8dff-caa19642bfe3

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Wulfram Gerstner, Julien Mayor
2003
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/cdaee27b-4564-4999-8dff-caa19642bfe3

About this result

Related concepts (30)

Recurrent neural network

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs makes them applicable to tasks such as unsegmented, connected handwriting recognition or speech recognition.

Types of artificial neural networks

There are many types of artificial neural networks (ANN). Artificial neural networks are computational models inspired by biological neural networks, and are used to approximate functions that are generally unknown. Particularly, they are inspired by the behaviour of neurons and the electrical signals they convey between input (such as from the eyes or nerve endings in the hand), processing, and output from the brain (such as reacting to light, touch, or heat). The way neurons semantically communicate is an area of ongoing research.

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

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