Los Angeles, CA
March 31, 2009 to April 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.470
According to the usual approximation scheme, we extend the Spike-Rate Perceptron to develop a more biologically plausible so-called Extended Spike-Rate Perceptron with renewal process inputs, which employs both first and second statistics, i.e. the means, variances and correlations of the synaptic input. We show that such perceptron, even a single neuron, is able to perform complex non-linear tasks like the XOR problem, which is impossible to be solved by traditional single-layer perceptrons. Here such perceptron offers a significant advantage over Spike-Rate Perceptrons, in that it includes a more accurate approximation to synaptic inputs, and that it introduces variance in the error representation. Our purpose is to open up the possibility of carrying out a random computation in neuronal networks.
Yingchun Deng, Xuyan Xiang, "Extended Spike-Rate Perceptrons", CSIE, 2009, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE, 2009 WRI World Congress on Computer Science and Information Engineering, CSIE 2009, pp. 33-37, doi:10.1109/CSIE.2009.470