In the method section of our paper, we describe the general encoding-decoding paradigm. We provide a brief overview of our data preprocessing pipeline, which involves the following steps. We employ the method of Boussard et al. (2021) to estimate the location of Decentralized registration (Windolf et al., 2022) is applied to track and correct Figure 6: Motion drift in "good" and "bad" sorting recordings. "bad" sorting example, which is still affected by drift even after registration. To decode binary behaviors, such as the mouse's left or right choices, we utilize In this section, we provide visualizations to gain insights into the effectiveness of our proposed decoder.

Unlike the case for text--wherein every document written in a given language shares a basic lexicon for tokenization--there is no one-to-one correspondence between neurons in different individuals.

This augmentation is applied to waveforms with a probability of 0.7. T emporal Jitter: This augmentation works through two steps. This augmentation is applied to waveforms with a probability of 0.5. We use a batch size of 128 and learning rate of 0.0001 for all multi-channel models. CEED benchmark models seems appropriate.

In CEED, we assume that waveforms are already extracted from an extracellular recording. Each waveform is then passed through our stochastic view generation module, where different views are obtained by applying transformations.