Training spiking neuronal networks with applications in engineering tasks
Rowcliffe, Phill and Feng, Jianfeng. (2008) Training spiking neuronal networks with applications in engineering tasks. IEEE Transactions on Neural Networks, Vol.19 (No.9). pp. 1626-1640. ISSN 1045-9227Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TNN.2008.2000999
In this paper, spiking neuronal models employing means, variances, and correlations for computation are introduced. We present two approaches in the design of spiking neuronal networks, both of which are applied to engineering tasks. In exploring the input-output relationship of integrate-and-fire (IF) neurons with Poisson inputs, we are able to define mathematically robust learning rules, which can be applied to multilayer and time-series networks. We show through experimental applications that it is possible to train spike-rate networks on function approximation problems and on the dynamic task of robot arm control.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Q Science > QP Physiology
T Technology > TK Electrical engineering. Electronics Nuclear engineering
|Divisions:||Faculty of Science > Centre for Scientific Computing
Faculty of Science > Computer Science
|Library of Congress Subject Headings (LCSH):||Neural circuitry, Neural networks (Computer science), Computational neuroscience, Robotics|
|Journal or Publication Title:||IEEE Transactions on Neural Networks|
|Number of Pages:||15|
|Page Range:||pp. 1626-1640|
|Access rights to Published version:||Restricted or Subscription Access|
Actions (login required)