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. 2014 May 28;8:57. doi: 10.3389/fncom.2014.00057

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Copyright © 2014 Savin and Triesch.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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The importance of reward-dependent learning for shaping circuit dynamics. (A) Performance comparison of networks shaped by r-STDP (blue) vs. circuits where the recurrent connectivity is static (green) in a simple delayed response task. (B) Performance comparison of networks shaped by r-STDP (blue) vs. circuits where the recurrent connectivity is shaped by reward-independent STDP (red) in a 2-class categorization task; K = 8, fixed delay. For all conditions, the learning of the decision output was reward-modulated. Dark colors mark averages across five repetitions; light colors show the standard deviation around this mean. (C) Neuronal selectivity to stimulus category when learning in the recurrent circuit is reward independent; K = 8, fixed delay (compare to Figure 3D).