Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity

TL;DR

This paper reviews how intrinsic and embedding dimensionality of neural activity can reveal computational principles, emphasizing their roles in understanding neural data and proposing network models as testing grounds.

Contribution

It clarifies the conceptual distinction between intrinsic and embedding dimensionality and discusses their potential to uncover neural computation mechanisms.

Findings

Intrinsic dimensionality reflects latent variables in neural activity.

Embedding dimensionality indicates how information is processed.

Network models serve as ideal tools for testing hypotheses.

Abstract

The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity, while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.