@article {Miconi057729, author = {Thomas Miconi}, title = {Flexible decision-making in recurrent neural networks trained with a biologically plausible rule}, elocation-id = {057729}, year = {2016}, doi = {10.1101/057729}, publisher = {Cold Spring Harbor Laboratory}, abstract = {Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Recurrent neural networks operating in the near-chaotic regime, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide the learning process. The lack of a biological learning method currently restricts the plausibility of recurrent networks as models of cortical computation. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial, for nontrivial tasks. We use this method to learn various tasks from the experimental literature, showing that this learning rule can successfully implement flexible associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. We show that the resulting networks exhibit complex dynamics previously observed in animal cortex, such as dynamic encoding and maintenance of task features, switching from stimulus-specific to response-specific representations, and selective integration of relevant input streams. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.}, URL = {https://www.biorxiv.org/content/early/2016/07/26/057729}, eprint = {https://www.biorxiv.org/content/early/2016/07/26/057729.full.pdf}, journal = {bioRxiv} }