Narrow Results

In this work we use Generalized Linear Models (GLMs) in order to identify a complex neurophysiological system, called muscle spindle that involves stationary point processes. Three parameters are of interest because they describe the intrinsic properties of the system: the threshold, the recovery and the summation function. These parameters are included in the GLMs and their estimates are obtained by using the maximum likelihood approach. Two cases are examined. In the first case, there is no input present and it is shown that the system fires spontaneously. In the second case, the system is affected by the presence of a gamma motoneurone. It is shown that there is no spontaneous activity and the behavior of the system is excitatory. These results are in accordance with previous work. In the present work a new parameter, the carry over effect function is included in the model. The performance of the new model is compared with the previous results and it is shown that the addition of the carry over effect function in the model improves the results significantly.

In this context, a model is an algorithm or a procedure that applies to data resulting in a functional relation $\tau$ between “input space” ${𝒳}$ and “output space” ${𝒴}$. In this short paper, we will delineate objective criteria which help to disambiguate and rate models’ credibility. We will define pertinent concepts and will voice an opinion on the matter of good versus bad versus so–so models.

In this context, a model is an algorithm or a procedure that applies to data resulting in a functional relation τ between “input space” $\mathcal{X}$ and “output space” $\mathcal{Y}$. In this short paper, we will delineate objective criteria which help to disambiguate and rate models’ credibility. We will define pertinent concepts and will voice an opinion on the matter of good versus bad versus so–so models.

Neural computing has emerged as a practical and powerful tool for "nonlinear" multivariate statistical analysis. In this paper, nonlinear discriminant analysis using a neural network is considered and applied to a medical diagnosis problem. The probabilistic interpretation of the network output is discussed in classification problems. The principle of the likelihood in network models is employed based on a probabilistic approach regarding the connection weights of the network as unknown parameters, and the maximum likelihood estimators of outputs are also introduced. Additionally, statistical techniques are formulated in terms of the principle of the likelihood of network models. The statistical tools for the inference illustrated here include i) deviance to evaluate the goodness-of-fit of a network model, ii) selection of the best fit model among several competing network models, and iii) likelihood-ratio chi-square statistics for pruning of neural network parameters and selection of a "best" subset of predictor variables.