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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,(More)
We consider how to determine all transition rates of an ionic channel when it can be conformationally described by a star-graph branch Markov chain with continuous time. It is found that all transition rates are uniquely determined by the distributions of their lifetime and death-time at the end state of each branch. An algorithm to exactly calculate all(More)
Long memory or long range dependency is an important phenomenon that may arise in the analysis of time series or spatial data. Most of the definitions of long memory of a stationary process X = {X 1 , X 2 , · · · , } are based on the second-order properties of the process. The excess entropy of a stationary process is the summation of redundancies which(More)
We have developed a totally different approach from Maximum likelihood widely employed to estimate the kinetic constants of single-ion channels of hierarchical mechanism. It is found that all kinetic constants are uniquely determined by the probability density functions of their lifetime and death-time of the middle states. An algorithm to calculate all(More)
We present the non-linear properties of Spike-Rate Perceptron with super-Poisson inputs, which employs both first and second statistical representation, i.e. the means, variances and correlations of the synaptic input. It shows that such perceptron, even a single neuron, is able to perform various complex non-linear tasks like the XOR problem. Here such(More)