<p>(A) The number of learning epochs required for correct learning as a function of the load , for . Correct learning was not achieved for I-learning and ReSuMe for larger than 0.03. (B) The number of learning epochs required for correct learning as a function of the number of input synapses . Correct learning was not achieved for I-learning for nor larger than 6,000. Averages and standard deviations over 500 realizations. The arrows indicate the conditions for which the parameters were optimized.</p
<p>(A): Learning speed when , , or , where N and T are the number of neurons and constrained tasks, ...
<p>The neuron is trained in a maximum number of 500 epochs to correctly memorize a set of 10 spike p...
The overfit problem in empirical learning and the utility problem in analytical learning both descri...
<p>The input patterns are classified into 3 classes. (A)–(C) The average minimum number of epochs re...
<p>The neuron is trained to memorize all patterns correctly in a maximum number of 500 epochs. The r...
<p> (A) The maximum load (the capacity ) as a function of the trial length . (B) The number of learn...
Existing metrics for the learning performance of feed-forward neural networks do not provide a satis...
<p> (A) The average minimum number of epochs required for correct learning, as a function of the loa...
<p>The number of learning epochs required for correct learning as a function of the load , for vario...
<p>A: Simulation results on different time lengths fixing the input spike rate to 10 Hz. B: Simulati...
Performance parameter values for five machine learning algorithms before and after over-sampling.</p
<p>(A): Learning speed when , or . The bar graph and error bars depict sample means and standard dev...
<p><b>A.</b> An example of the variations of the error (blue) and the noiseless error (red) with the...
<p>(A) An example set of generative fields for unconstrained (left column) and normalized (right col...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
<p>(A): Learning speed when , , or , where N and T are the number of neurons and constrained tasks, ...
<p>The neuron is trained in a maximum number of 500 epochs to correctly memorize a set of 10 spike p...
The overfit problem in empirical learning and the utility problem in analytical learning both descri...
<p>The input patterns are classified into 3 classes. (A)–(C) The average minimum number of epochs re...
<p>The neuron is trained to memorize all patterns correctly in a maximum number of 500 epochs. The r...
<p> (A) The maximum load (the capacity ) as a function of the trial length . (B) The number of learn...
Existing metrics for the learning performance of feed-forward neural networks do not provide a satis...
<p> (A) The average minimum number of epochs required for correct learning, as a function of the loa...
<p>The number of learning epochs required for correct learning as a function of the load , for vario...
<p>A: Simulation results on different time lengths fixing the input spike rate to 10 Hz. B: Simulati...
Performance parameter values for five machine learning algorithms before and after over-sampling.</p
<p>(A): Learning speed when , or . The bar graph and error bars depict sample means and standard dev...
<p><b>A.</b> An example of the variations of the error (blue) and the noiseless error (red) with the...
<p>(A) An example set of generative fields for unconstrained (left column) and normalized (right col...
The performance of the proposed model at two extreme thresholds with different sparsity.</p
<p>(A): Learning speed when , , or , where N and T are the number of neurons and constrained tasks, ...
<p>The neuron is trained in a maximum number of 500 epochs to correctly memorize a set of 10 spike p...
The overfit problem in empirical learning and the utility problem in analytical learning both descri...