کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410971 679175 2006 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning under weight constraints in networks of temporal encoding spiking neurons
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Learning under weight constraints in networks of temporal encoding spiking neurons
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 69, Issues 16–18, October 2006, Pages 1912–1922
نویسندگان
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