When predict can also explain: Few-shot prediction to select better neural latents

TL;DR

This paper introduces a new method to improve the accuracy of neural latent variable models by addressing the issue of extraneous dynamics in their predictions.

Contribution

A novel secondary metric, few-shot co-smoothing, is proposed to filter out models with extraneous dynamics.

Findings

Models with high co-smoothing scores can still contain arbitrary extraneous dynamics.

Few-shot co-smoothing identifies minimal models without extraneous dynamics.

The new metric correlates with a novel validation measure based on cross-decoding.

Abstract

Latent variable models serve as powerful tools to infer underlying dynamics from observed neural activity. Ideally, the inferred dynamics should align with true ones. However, due to the absence of ground truth data, prediction benchmarks are often employed as proxies. One widely-used method, co-smoothing, involves jointly estimating latent variables and predicting observations along held-out channels to assess model performance. In this study, we reveal the limitations of the co-smoothing prediction framework and propose a remedy. Using a student-teacher setup, we demonstrate that models with high co-smoothing can have arbitrary extraneous dynamics in their latent representations. To address this, we introduce a secondary metric—few-shot co-smoothing, performing regression from the latent variables to held-out neurons in the data using fewer trials. Our results indicate that among models with near-optimal co-smoothing, those with extraneous dynamics underperform in the few-shot co-smoothing compared to ‘minimal’ models that are devoid of such dynamics. We provide analytical insights into the origin of this phenomenon and further validate our findings on four standard neural datasets using a state-of-the-art method: STNDT. In the absence of ground truth, we suggest a novel measure to validate our approach. By cross-decoding the latent variables of all model pairs with high co-smoothing, we identify models with minimal extraneous dynamics. We find a correlation between few-shot co-smoothing performance and this new measure. In summary, we present a novel prediction metric designed to yield latent variables that more accurately reflect the ground truth, offering a significant improvement for latent dynamics inference.

The availability of large scale neural recordings encourages the development of methods to fit models to data. How do we know that the fitted models are loyal to the true underlying dynamics of the brain? A common approach is to use prediction scores that use one part of the available data to predict another part. The advantage of predictive scores is that they are general: a wide variety of modelling methods can be evaluated and compared against each other. But does a good predictive score guarantee that we capture the true dynamics in the model? We investigate this by generating synthetic neural data from one model, fitting another model to it, ensuring a high predictive score, and then checking if the two are similar. The result: only partially. We find that the high scoring models always contain the truth, but may also contain additional ‘made-up’ features. We remedy this issue with a secondary score that tests the model’s generalisation to another set of neurons with just a few examples. We demonstrate its applicability with synthetic and real neural data.

Figures