<p>The upper row shows how the proportion of networks that converged varies as function of <i>β</i> (learning rate) and <i>λ</i> (decay of tags); white regions had a proportion of convergence lower than 0.8. The lower row shows the effect of <i>β</i> and <i>λ</i> on the median trial when the learning criterion was reached; white regions reached convergence later than the yellow regions (see insets).</p
<p>A. Left: Evolution of escape latency as a function of trials, without lateral connections () and ...
Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes...
<p><i>(A)</i> Multilayer modularity, <i>(B)</i> number of communities, and <i>(C)</i> mean flexibili...
<p><b><i>A</i></b>, Scaling with unchanged learning parameters <i>β</i> and <i>λ</i>. Left, converge...
<p><b>(A, B)</b> The robustness of the results in the default configuration of the network is studie...
<p>Scatter plots of population hit rate and response time in the first versus last segment of trials...
<p><b>A</b> Simulated set of 45 movement endpoints if learning rate <i>B</i> = 0 (i.e., no correctio...
For each of the 16 populations presented in Fig 4, one network was chosen at random at the end of th...
Parameter settings and graphical conventions are as in Fig 3. In (A), the performance of the evolved...
<p>(A) Change in robustness, measured as the difference of the mean phenotypic distances between unp...
<p>A. End-of-trial errors under different learning conditions: no learning, reward-only, punisher-on...
<p>A) Variability of response times for both probe blocks. Errorbars show standard error across subj...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
<p>The statistical threshold was set at <i>p</i><0.05, corrected for multiple comparisons at the clu...
<p>Colour maps show the network state as a function of pairs of parameters (other parameters set to ...
<p>A. Left: Evolution of escape latency as a function of trials, without lateral connections () and ...
Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes...
<p><i>(A)</i> Multilayer modularity, <i>(B)</i> number of communities, and <i>(C)</i> mean flexibili...
<p><b><i>A</i></b>, Scaling with unchanged learning parameters <i>β</i> and <i>λ</i>. Left, converge...
<p><b>(A, B)</b> The robustness of the results in the default configuration of the network is studie...
<p>Scatter plots of population hit rate and response time in the first versus last segment of trials...
<p><b>A</b> Simulated set of 45 movement endpoints if learning rate <i>B</i> = 0 (i.e., no correctio...
For each of the 16 populations presented in Fig 4, one network was chosen at random at the end of th...
Parameter settings and graphical conventions are as in Fig 3. In (A), the performance of the evolved...
<p>(A) Change in robustness, measured as the difference of the mean phenotypic distances between unp...
<p>A. End-of-trial errors under different learning conditions: no learning, reward-only, punisher-on...
<p>A) Variability of response times for both probe blocks. Errorbars show standard error across subj...
<p>The learning trend versus trial number for the conditions of Experiment 2 and Experiment 3 plus a...
<p>The statistical threshold was set at <i>p</i><0.05, corrected for multiple comparisons at the clu...
<p>Colour maps show the network state as a function of pairs of parameters (other parameters set to ...
<p>A. Left: Evolution of escape latency as a function of trials, without lateral connections () and ...
Adaptive networks consist of a collection of nodes with adaptation and learning abilities. The nodes...
<p><i>(A)</i> Multilayer modularity, <i>(B)</i> number of communities, and <i>(C)</i> mean flexibili...