RT Journal Article SR Electronic T1 Simple Framework for Constructing Functional Spiking Recurrent Neural Networks JF bioRxiv FD Cold Spring Harbor Laboratory SP 579706 DO 10.1101/579706 A1 Robert Kim A1 Yinghao Li A1 Terrence J. Sejnowski YR 2019 UL http://biorxiv.org/content/early/2019/03/15/579706.abstract AB Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. We validate our framework on several cognitive task paradigms to replicate previously observed experimental results. We also demonstrate different ways to exploit the biological features of our models to elucidate neural mechanisms underlying cognitive functions.