TY - JOUR T1 - Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory JF - bioRxiv DO - 10.1101/842302 SP - 842302 AU - Robert Kim AU - Terrence J. Sejnowski Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/11/15/842302.abstract N2 - Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli. Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally. Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge. ER -