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. Previous studies have shown that higher cortical areas such as prefrontal cortex operate on a long timescale, measured as the spike-count autocorrelation decay constant at rest [1] . These long timescales have been hypothesized to be critical for performing working memory (WM) computations [2, 3] , but it is experimentally challenging to probe the underlying circuit mechanisms that lead to stable temporal properties. Recurrent neural network (RNN) models trained to perform WM tasks could be a useful tool if these models also utilize units with long heterogeneous timescales and capture previous experimental findings. However, such RNN models have not yet been identified. In this study, we construct a spiking RNN model to perform a WM task and compare the emerging timescales with the timescales derived from the prefrontal cortex of rhesus monkeys trained to perform similar WM tasks. We show that both macaque prefrontal cortex and the RNN model utilize units/neurons with long timescales during delay period to sustain stimulus information. In addition, the number of units with long timescales was significantly reduced in the RNN model trained to perform a non-WM task, further supporting the idea that neuronal timescales are task-specific and functionally organized. In this study, we employed a spiking RNN model of WM to investigate if the model exhibits and utilizes heterogeneous timescales for prolonged integration of information. We validated the model using an experimental dataset obtained from rhesus monkeys trained on WM tasks: the model and the primate prefrontal cortex both displayed similar heterogeneous neuronal timescales and incorporated units/neurons with long timescales to maintain stimulus information. The timescales from the RNN model trained on a non-WM task (Go-NoGo task) were markedly shorter, since units with long timescales were not required to support the simple computation. Future works include characterizing the network dynamics and the circuit motifs of the DMS RNN model to elucidate connectivity structures required to give rise to the diverse, stable temporal receptive fields specific to WM.
Spiking recurrent neural networks performing a working memory task utilize long heterogeneous timescales, strikingly similar to those observed in prefrontal cortex.
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