Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410971 | Neurocomputing | 2006 | 11 Pages |
Limits on synaptic efficiency are characteristic of biological neural networks. In this paper, weight limitation constraints are applied to the spike time error-backpropagation (SpikeProp) algorithm for temporally encoded networks of spiking neurons. A novel solution to the problem raised by non-firing neurons is presented which makes the learning algorithm converge reliably and efficiently. In addition a square cosine encoder is applied to the input neurons to reduce the number of input neurons required. The approach is demonstrated by application to the classical XOR-problem analysis, a function approximation experiment and benchmark data sets. Using input delay neurons and relative timing, the algorithm is also applied to solve a time series prediction problem. The experimental results show that the new approach produces comparable accuracy in classification with the original approach while utilising a smaller spiking neural network.