PubMed · 4969718
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T Sugouchi. 1968. [Self-learning through strict discipline].. https://pubmed.ncbi.nlm.nih.gov/4969718/
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The basis for this neuronal model is that the properties of excitable membranes are controlled by biochemical reactions occurring in the nerve cells. The kinetics of these supposed chemical reactions is described by a set of first order differential equations. We considered the effect of regulation of the properties of sodium channels. This allows the simulation of the changes in the neuron's electrical activity parameters occurring during learning, associated with its excitability. The neuronal model exhibits different excitability after the learning procedure relative to the different input signals that corresponds to the experimental data.
We study the dynamics of on-line learning for a large class of neural networks and learning rules, including backpropagation for multilayer perceptrons. In this paper, we focus on the case where successive examples are dependent, and we analyze how these dependencies affect the learning process. We define the representation error and the prediction error. The representation error measures how well the environment is represented by the network after learning. The prediction error is the average error that a continually learning network makes on the next example. In the neighborhood of a local minimum of the error surface, we calculate these errors. We find that the more predictable the example presentation, the higher the representation error, i.e., the less accurate the asymptotic representation of the whole environment. Furthermore we study the learning process in the presence of a plateau. Plateaus are flat spots on the error surface, which can severely slow down the learning process. In particular, they are notorious in applications with multilayer perceptrons. Our results, which are confirmed by simulations of a multilayer perceptron learning a chaotic time series using backpropagation, explain how dependencies between examples can help the learning process to escape from a plateau.
We model a two-eye visual environment composed of natural images and study its effect on single cell synaptic modification. In particular, we study the effect of binocular cortical misalignment on receptive field formation after eye opening. We show that binocular misalignment affects principal component analysis (PCA) and Bienenstock, Cooper, and Munro (BCM) learning in different ways. For the BCM learning rule this misalignment is sufficient to produce varying degrees of ocular dominance, whereas for PCA learning binocular neurons emerge in every case.