Characteristic snapshots of cooperators (red) and defectors (blue) under the prepared initial state for different times steps.

<p>From top row to bottom panel, the tunable parameter is set to be 0, 0.5 and 1.0, respectively. In all dynamical patterns, the synergy factor is 0.6, lattice size is 200 and strategy adoption uncertainty is 0.1.</p

Full normalized phase diagrams for different tunable parameters .

<p>From top to bottom, panels (a), (b), (c) correspond to , respectively. The lattice size is and PGG group size is fixed to be .</p

Top panel: time courses depicting the evolution of cooperation for different values of under the random distribution of strategies.

<p>Note that the arrows denote the end of enduring (END) period and the beginning of expanding (EXP) period. Bottom panel: the evolution snapshots of different values of . From top to bottom, the values of are 0, 0.5, 1.0 and 2.0, respectively. The colore code is the same with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091012#pone-0091012-g002" target="_blank">Fig.2:</a> cooperator (red) and defector (blue). From left to right, the time steps are 0, 10, 100, 1000 for each value of .</p

Fraction of cooperators as a function of normalized enhancement factor on triangular lattices in which and as that in Ref.[52].

<p>Other parameters are identical with those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0091012#pone-0091012-g001" target="_blank">Fig.1</a>.</p

The locally-linked neuronal network model and external asynchronous stimulus currents.

<p>(a) The locally-linked neuronal network model on two-dimensional square having neurons and layers. Here, we take , and label neural number (1 – 1000) from left to right layers. (b) To train the network, the input pulse current with duration is injected alternately into each pair of layers with the left-right sequence having the same inter-stimulus interval , respectively. After each training trial, there is a long enough time to let network activity recover to the rest state for the next training. We perform training for all the pair of layers with the same number of trials. (c) To test the resulting feedforward structure and its propagative capacity, a steady current is injected into a certain layer (here, we choose the 3rd layer, i.e., L3).</p

Distribution of synaptic weights after the different training trials.

<p>Distribution of synaptic weights for three different connection types: feedforward (left), feedback (middle) and recurrent (right) at the beginning of training (a) and after the training with different number of trials 10 (b), 20 (c), 30 (d). The same parameters are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g002" target="_blank">Fig. 2</a>, except for ms.</p

Average synaptic weights as a function of the stimulus interval for STDP with temporal asymmetry.

<p>The same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g006" target="_blank">Fig. 6</a>, but for the temporally asymmetric form of STDP. Here, the parameters are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g002" target="_blank">Fig. 2</a>, except for , ms, ms.</p

Dependance of exponents for the resulting structure on exponents of STDP after 20 training trials.

<p>(a) vs. for different (b) vs. for different . For comparison, and are shown as solid lines in (a) and (b), respectively. The other parameters are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g002" target="_blank">Fig. 2</a>.</p

Evolution of the network and dynamical propagation.

<p>The weights of synaptic strengths on the model before (a) and after (b) training trials. (c) The spatiotemporal pattern of neuron spikes by injecting steady current for testing feedforward network. The system parameters are , and ms. The parameters for training (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g001" target="_blank">Fig. 1(b)</a>) and testing (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g001" target="_blank">Fig. 1(c)</a>) are given by , ms, s, ms, and .</p

Distribution of synaptic weights after the different training stimulus intervals.

<p>Distribution of synaptic weights for different connection types: feedforward (left), feedback (middle) and recurrent (right) after the 20 training trials with different stimulus intervals ms (a), ms (b), ms (c), ms (d). The other parameters are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084644#pone-0084644-g002" target="_blank">Fig. 2</a>.</p