Feng, J., Sun, Y.L. and Buxton, H. (2003) Training integrate-and-fire neurons with the Informax principle II. IEEE Transactions on Neural Networks, 14 (2). pp. 326-336. ISSN 1045-9227Full text not available from this repository.
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.
|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:||30 Nov 2012 16:51|
|Google Scholar:||14 Citations|