University of Sussex
Browse

File(s) not publicly available

Training integrate-and-fire neurons with the Informax principle II

journal contribution
posted on 2023-06-07, 14:02 authored by J. Feng, Y.L. Sun, Hilary Buxton
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.

History

Publication status

  • Published

Journal

IEEE Transactions on Neural Networks

ISSN

1045-9227

Issue

2

Volume

14

Page range

326-336

Department affiliated with

  • Informatics Publications

Full text available

  • No

Peer reviewed?

  • Yes

Legacy Posted Date

2007-07-27

Usage metrics

    University of Sussex (Publications)

    Categories

    No categories selected

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC