ABSTRACT
Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax, and sequential approaches. Here we present a genuinely symmetric criterion based on a linearized distance between mean-value surfaces and the newly introduced tool of flexible nominal sets. We demonstrate the computational efficiency of the approach using the proposed criterion and provide a Monte-Carlo evaluation of its discrimination performance on the basis of the likelihood ratio. An application for a pair of competing models in enzyme kinetics is given.
Subject(s)
Biometry/methods , Models, Statistical , Bayes Theorem , Likelihood Functions , Monte Carlo MethodABSTRACT
We propose a network of model neurones that "reads" the information encoded as a mean spiking rate by mechanisms relevant to the organism. The streams of independent irregular spiking activity with a Poisson distribution enters the network in parallel via two inputs. The network integrates both synaptic inputs and at the same time acts as a counter allowing their continuous comparison. Detection of the mean spiking rate difference is signalled by spikes emitted at the output. The exactness of the mean-rate discrimination was quantified by the probability of theoretically best comparison.
