Sufficient conditions for offline reactivation in recurrent neural networks
This work provides theoretical insights into offline reactivation in neuroscience, addressing a fundamental question for researchers modeling brain circuits, but it is incremental as it builds on existing frameworks.
The study tackled the problem of understanding when task-optimized neural networks can autonomously reactivate states during offline periods, and found that noisy recurrent networks optimized to track environmental variables naturally develop denoising dynamics that cause reactivation, validated through numerical experiments on spatial position and head direction estimation tasks.
During periods of quiescence, such as sleep, neural activity in many brain circuits resembles that observed during periods of task engagement. However, the precise conditions under which task-optimized networks can autonomously reactivate the same network states responsible for online behavior is poorly understood. In this study, we develop a mathematical framework that outlines sufficient conditions for the emergence of neural reactivation in circuits that encode features of smoothly varying stimuli. We demonstrate mathematically that noisy recurrent networks optimized to track environmental state variables using change-based sensory information naturally develop denoising dynamics, which, in the absence of input, cause the network to revisit state configurations observed during periods of online activity. We validate our findings using numerical experiments on two canonical neuroscience tasks: spatial position estimation based on self-motion cues, and head direction estimation based on angular velocity cues. Overall, our work provides theoretical support for modeling offline reactivation as an emergent consequence of task optimization in noisy neural circuits.