Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

摘要

Author Summary Cognitive functions arise from the coordinated activity of many interconnected neurons. As neuroscientists increasingly use large datasets of simultaneously recorded neurons to study the brain, one approach that has emerged as a promising tool for interpreting population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as recorded animals. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them in arbitrary ways, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features that are essential if RNNs are to provide insights into the circuit-level operation of the brain. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here we describe and provide an implementation for such a framework, which we apply to several well-known experimental paradigms that illustrate the diversity of detail that can be modeled. Our work provides a foundation for neuroscientists to harness trained RNNs in their own investigations of the neural basis of cognition.