Biomedical subjects
Publications and source records attributed to E N Miranda.
The capacity of a layered neural network for learning hetero-associations is studied numerically as a function of the number M of hidden neurons. We find that there is a sharp change in the learning ability of the network as the number of hetero-associations increases. This fact allows us to define a maximum capacity C for a given architecture. It is found that C grows logarithmically with M.
A formal neuron has been studied mathematically. The spiking behaviour of a single neuron has been considered and the influence of the other neurons has been replaced by an average activity level. Four different kinds of spiking behaviour are predicted by the model: B (bursts), C (continuous), P (periodic) and S (silent) neurons and several real neurons can be classified within these four categories. Some properties of the spiking neuron are calculated: 1) the time between spikes, 2) the spike train length and 3) the silent time. Because these magnitudes can be measured in the laboratory, an experimental validation of the model is proposed.
A multilayer neural network has been used for deciding which oil reservoir layer has to be perforated. Many network architectures were tested until we found those with the best generalization capability. The network performs better than human experts and its achievements are higher than the historical average in the test area. As in other applications of neural networks, the learning capability improves with more hidden neurons but the generalization does not.