Cortical neurons process information on multiple timescales, and neurons that remain active during delay periods lasting seconds have been recorded in the parietal and prefrontal cortex. However, the underlying mechanisms for the emergence of neuronal timescales stable enough to support working memory are unclear. A spiking recurrent neural network (RNN) model was trained on a working memory task and unit activity was compared with single neurons in the primate prefrontal cortex. The temporal properties of our model and the neural data are remarkably similar. Analysis of the RNN model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and working memory maintenance. Enhancing inhibitory-to-inhibitory connections led to more stable temporal dynamics and improved task performance. The same network can perform other tasks without disrupting its pre-existing timescale architecture, suggesting that strong inhibitory signaling underlies a flexible working memory network.
1. To learn a new approach to understanding brain function by creating networks to perform tasks using machine learning
2. To learn how how to probe these networks to discover how they solve a task
3. To learn how these models can be used to formulate experimentally testable hypotheses