NeuralRNN: a unified framework for recurrent neural network methods in cognitive neuroscience

Abstract

Recurrent neural networks (RNNs) provide a central tool in neuroscience to optimize cognitive tasks and reconstruct dynamical systems. Around these two paradigms, many RNN model variants have been tailored to capture specific computations of the brain. In practice, however, this methodological diversity remains difficult to exploit, because different paradigms and model variants are mostly implemented in idiosyncratic, study-specific code. Here we introduce NeuralRNN, an open-source Python framework that unifies these paradigms and model variants in a general-purpose pipeline. NeuralRNN brings task optimization, dynamical system reconstruction, and biological constraints into a single objective interface. It also provides an integrated and extensible model library spanning generic, structure-constrained, gain-modulated, and gated RNNs. Furthermore, data, models, and training are constructed through a shared automated pipeline and are equipped with a suite of tools for dynamical systems analysis. Together, NeuralRNN lowers the barrier to implementing, comparing, and extending RNN methods in neuroscience.