Withoutrequiring repeatable trials, itcanflexibly capture covariate-dependent jointSCDs, andprovide interpretable latent causes underlying the statistical dependencies between neurons.

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.

Many animals learn cognitive maps of their environment - a simultaneous representation of context, experience, and position.

Further experimental details are described in Ref. [4]. Each spatiotemporal stimulus spanned over 400 ms corresponding to the retinal integration timescale. Figure 1: (a) The log-likelihood of empirical data for each PMF averaged over cells. Black line is the identity line. The central 20 20 arrays are shown.

Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories.

We develop and test two potential solutions: an alternate validation method ("sample validation") and a novel regularization method ("coordinated dropout").

Neural Information Processing Systems http://nips.cc/