# Statistical Neuroscience in the Single Trial Limit

arXiv: https://arxiv.org/abs/2103.05075 · HTML: https://mlsanity.com/paper/2103.05075/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Risk and cross validation in ridge regression with correlated samples](https://mlsanity.com/paper/2408.04607/) (2024)
- [Estimating Shape Distances on Neural Representations with Limited Samples](https://mlsanity.com/paper/2310.05742/) (2023)
- [Modeling Neural Activity with Conditionally Linear Dynamical Systems](https://mlsanity.com/paper/2502.18347/) (2025)
- [Characterizing higher-order representations through generative diffusion models explains human decoded neurofeedback performance](https://mlsanity.com/paper/2503.14333/) (2025)

## Abstract (arXiv)

Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure -- including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions -- and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.