68 research outputs found
On the Biological Plausibility of Artificial Metaplasticity
The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probabilit
Stability of Negative Image Equilibria in Spike-Timing Dependent Plasticity
We investigate the stability of negative image equilibria in mean synaptic
weight dynamics governed by spike-timing dependent plasticity (STDP). The
neural architecture of the model is based on the electrosensory lateral line
lobe (ELL) of mormyrid electric fish, which forms a negative image of the
reafferent signal from the fish's own electric discharge to optimize detection
of external electric fields. We derive a necessary and sufficient condition for
stability, for arbitrary postsynaptic potential functions and arbitrary
learning rules. We then apply the general result to several examples of
biological interest.Comment: 13 pages, revtex4; uses packages: graphicx, subfigure; 9 figures, 16
subfigure
Multilayered feed forward Artificial Neural Network model to predict the average summer-monsoon rainfall in India
In the present research, possibility of predicting average summer-monsoon
rainfall over India has been analyzed through Artificial Neural Network models.
In formulating the Artificial Neural Network based predictive model, three
layered networks have been constructed with sigmoid non-linearity. The models
under study are different in the number of hidden neurons. After a thorough
training and test procedure, neural net with three nodes in the hidden layer is
found to be the best predictive model.Comment: 19 pages, 1 table, 3 figure
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