6533b7dafe1ef96bd126ec6c
RESEARCH PRODUCT
A New Unsupervised Neural Network for Pattern Recognition with Spiking Neurons
C. AntonioR. RiccardoR. Lorenzosubject
Artificial neural networkComputer scienceGeneralizationbusiness.industryStability (learning theory)Pattern recognitionMemorizationmedicine.anatomical_structureSPike neural netwroksPattern recognition (psychology)medicineNeuronArtificial intelligenceLayer (object-oriented design)Representation (mathematics)businessdescription
In this paper we propose a three-layered neural network for binary pattern recognition and memorization. Unlike the classic approach to pattern recognition, our net works organizing itself in an unsupervised way, to distinguish beetween different patterns or to recognize similar ones. If we present a binary input to the first layer, after some time steps we could read the output of the net in the third layer, as one and only one neuron activating with high firing rate; the middle layer will act as a generalization layer, i.e. similar pattern will have similar (or the same) representation in the middle layer. We used learning algorithms inspired from other works or from biological data to achieve network stability and a correct pattern memorization. The network can be used for pattern recognition or generalization by selecting output signals from the selection layer or the generalization layer.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2006-10-30 | The 2006 IEEE International Joint Conference on Neural Network Proceedings |