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Influence of the learning method in the performance of feedforward neural networks when the activity of neurons is modified
Neural and Evolutionary Computing · Submitted 21 Apr 2014
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We demonstrate that EA is much more robust than BP for all the cases under study.
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The abstract, sentence by sentence
A method that allows us to give a different treatment to any neuron inside feedforward neural networks is presented. The algorithm has been implemented with two very different learning methods: a standard Back-propagation (BP) procedure and an evolutionary algorithm. First, we have demonstrated that the EA training method converges faster and gives more accurate results than BP. Then we have made a full analysis of the effects of turning off different combinations of neurons after the training phase. We demonstrate that EA is much more robust than BP for all the cases under study. Even in the case when two hidden neurons are lost, EA training is still able to give good average results. This difference implies that we must be very careful when pruning or redundancy effects are being studied since the network performance when losing neurons strongly depends on the training method. Moreover, the influence of the individual inputs will also depend on the training algorithm. Since EA keeps a good classification performance when units are lost, this method could be a good way to simulate biological learning systems since they must be robust against deficient neuron performance. Although biological systems are much more complex than the simulations shown in this article, we propose that a smart training strategy such as the one shown here could be considered as a first protection against the losing of a certain number of neurons.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | A method that allows us to give a different treatment to any neuron inside feedforward neural networks is presented. | contribution 0.85 | 0.86 | 0.04 | asserted 0.99 |
| s1 | The algorithm has been implemented with two very different learning methods: a standard Back-propagation (BP) procedure and an evolutionary algorithm. | method 0.97 | 0.89 | 0.03 | asserted 1.00 |
| s2 | First, we have demonstrated that the EA training method converges faster and gives more accurate results than BP. | result 1.00 | 0.94 | 0.16 | asserted 1.00 |
| s3 | Then we have made a full analysis of the effects of turning off different combinations of neurons after the training phase. | contribution 0.53 | 0.96 | 0.04 | asserted 0.96 |
| s4 | We demonstrate that EA is much more robust than BP for all the cases under study. | result 1.00 | 0.94 | 0.45 | asserted 1.00 |
| s5 | Even in the case when two hidden neurons are lost, EA training is still able to give good average results. | result 1.00 | 0.94 | 0.12 | asserted 0.99 |
| s6 | This difference implies that we must be very careful when pruning or redundancy effects are being studied since the network performance when losing neurons strongly depends on the training method. | interpretation 1.00 | 0.84 | 0.57 | asserted 0.75 |
| s7 | Moreover, the influence of the individual inputs will also depend on the training algorithm. | result 0.64 | 0.68 | 0.09 | asserted 0.94 |
| s8 | Since EA keeps a good classification performance when units are lost, this method could be a good way to simulate biological learning systems since they must be robust against deficient neuron performance. | interpretation 0.98 | 0.38 | 0.18 | hedged possibility 0.99 |
| s9 | Although biological systems are much more complex than the simulations shown in this article, we propose that a smart training strategy such as the one shown here could be considered as a first protection against the losing of a certain number of neurons. | future work 0.54 | 0.19 | 0.15 | hedged possibility 0.91 |
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The paper
Original abstract
A method that allows us to give a different treatment to any neuron inside feedforward neural networks is presented. The algorithm has been implemented with two very different learning methods: a standard Back-propagation (BP) procedure and an evolutionary algorithm. First, we have demonstrated that the EA training method converges faster and gives more accurate results than BP. Then we have made a full analysis of the effects of turning off different combinations of neurons after the training phase. We demonstrate that EA is much more robust than BP for all the cases under study. Even in the case when two hidden neurons are lost, EA training is still able to give good average results. This difference implies that we must be very careful when pruning or redundancy effects are being studied since the network performance when losing neurons strongly depends on the training method. Moreover, the influence of the individual inputs will also depend on the training algorithm. Since EA keeps a good classification performance when units are lost, this method could be a good way to simulate biological learning systems since they must be robust against deficient neuron performance. Although biological systems are much more complex than the simulations shown in this article, we propose that a smart training strategy such as the one shown here could be considered as a first protection against the losing of a certain number of neurons.
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