Performance and evolution of the readout weights during bisection training.

<p>(A) Fraction of erroneous network decisions against training week, with a ‘week’ consisting of the presentation of bisection stimuli of fixed outer-line-distance (‘width’) but with randomized positions. Upon each stimulus presentation, the readout weights from the L5 and the L2/3 pyramidal neurons to the decision unit were changed according to an error correcting learning rule. A top-down induced gain increase in the L2/3 pyramidal neurons reduces the error level (grey: gain factor ; black: gain factor ). Hence, a substantial improvement in performance is achieved if learning simultaneously increases the top-down input strength, leading to a learning curve which interpolates between the two curves (dashed line). The fast initial learning progress arises from adapting the readout connections to the decision unit. (B) Learning curve for a monkey performing the bisection task (adapted from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000617#pcbi.1000617-Crist2" target="_blank">[10]</a>). (C) Synaptic weights from L2/3 pyramidal neurons to the decision unit before (circles) and after learning with (black) and without gain increase (grey). The dotted line indicates the universal weight distribution inferred in the theoretical argument. (D) Same as in C, but for synaptic weights from L5 pyramidal neurons to the decision unit. Error bars represent standard error of the mean (using learning runs).