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. 2011 May 12;7(5):e1001133. doi: 10.1371/journal.pcbi.1001133

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Potjans et al.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.

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Average latency in reaching the reward state and standard deviations over runs for the neuronal network model with optimized parameters , , and reward (red curve) and the corresponding classical discrete-time algorithmic TD implementation with , , and reward (blue curve). Each data point shows the average latency over successive trials. Inset: grid-world environment consisting of states. Only the state marked with an asterisk is rewarded. In each state the agent (A) can choose between directions (indicated by the arrows). Once the rewarded state has been found, the agent is moved randomly to a new starting position.

Inline graphic — Average latency in reaching the reward state and standard deviations over runs for the neuronal network model with optimized parameters , , and reward (red curve) and the corresponding classical discrete-time algorithmic TD implementation with , , and reward (blue curve). Each data point shows the average latency over successive trials. Inset: grid-world environment consisting of states. Only the state marked with an asterisk is rewarded. In each state the agent (A) can choose between directions (indicated by the arrows). Once the rewarded state has been found, the agent is moved randomly to a new starting position.