Time-series prediction with single integrate-and-fire neuron
Applied Soft Computing
Learning with single quadratic integrate-and-fire neuron
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Classification of gene expression data using Spiking Wavelet Radial Basis Neural Network
Expert Systems with Applications: An International Journal
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For pt I see J. Phys. A, vol. 35, p. 2379-94 (2002).We develop neuron learning rules using the Informax principle together with the input-output relationship of the integrate-and-fire (IF) model with Poisson inputs. The learning rule is then tested with constant inputs, time-varying inputs and images. For constant inputs, it is found that, under the Informax principle, a network of IF models with initially all positive weights tends to disconnect some connections between neurons. For time-varying inputs and images, we perform signal separation tasks called independent component analysis. Numerical simulations indicate that some number of inhibitory inputs improves the performance of the system in both biological and engineering senses.