Feng, J., Sun, Y.L. and Buxton, Hilary (2003) Training integrate-and-fire neurons with the Informax principle II. IEEE Transactions on Neural Networks, 14 (2). pp. 326-336. ISSN 1045-9227

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Abstract

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.

Item Type:	Article
Keywords:	Informax principle, Poisson inputs, Independent component analysis, Integrate-and-fire model, Learning rule, Neuron learning, Neuron models, Single neurons, Time-varying inputs
Schools and Departments:	School of Engineering and Informatics > Informatics
Subjects:	Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Depositing User:	Chris Keene
Date Deposited:	27 Jul 2007
Last Modified:	17 Sep 2019 09:20
URI:	http://sro.sussex.ac.uk/id/eprint/1249
Google Scholar:	14 Citations

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