Title: Deep High-Frequency Extrapolation for Neuronal Spike Restoration. Abstract: Recording neuronal activity using multiple electrodes has been widely used for studying functional mechanisms of the brain. However, handling massive amounts of data is still a challenge. In this paper, we propose a novel strategy to restore high-frequency neuronal spikes from small-volume and low-frequency band signals. Inspired by the fact that high-frequency extrapolation is equivalent to super-resolution problems in 2D signals, we applied a Swin transformer to extrapolate high-frequency information from downsampled neuronal signals both in vitro and in vivo. We found that aliasing components of input signals and the spike jittering-based selection of the training batch improved the performance of reconstructing accurate neuronal spikes. As a result, we observed reasonably restored neuronal spiking activity, including the spike timing, waveforms, and network connectivity, even with the $\times 8$ subsampled dataset.
