Sequential Temporal Anticipation Characterized by Neural Power Modulation and in Recurrent Neural Networks

Relevant prospective moments arise intermittently, while most of time is filled with irrelevant events, or noise, that constantly bombard our sensory systems. Thus, anticipating a few key moments necessitates disregarding what lies between the present and the future – the noise. Here, through examining how the brain and the recurrent neural networks (RNNs) anticipate a sequence of prospective moments without relying on any external timing cues, we provided a reinterpretation of temporal anticipation. We first designed a “premembering” task, where we marked three temporal locations in white noise and asked human listeners to detect a tone at one of these points. Using power modulation analyses, we investigated the memory-guided anticipatory processes in trials involving only flat noise. Our research revealed a unique neural-power modulation pattern for sequential temporal anticipation: neural power within the alpha-beta band range fluctuates over time, accurately identifying the marked locations on a sub-second scale and correlating with tone detection performance. To understand the functional roles of these neural modulations, we utilized RNNs optimized for the behavioral task. The hidden dynamics of the RNNs mirrored the neural modulations, and additional analyses and perturbations on the RNNs indicated that the neural power modulations in the alpha-beta band resulted from selective suppression of irrelevant noise periods and heightened sensitivity to anticipated temporal locations. Our behavioral, neural, and modeling findings collectively indicate that sequential temporal anticipation involves a process of dynamic gain control: anticipating a few significant moments involves actively disregarding irrelevant events that frequently occur. ### Competing Interest Statement The authors have declared no competing interest.