Speaker
Description
In recent years, neural activity has increasingly been investigated from a geometric viewpoint, analyzing the neural manifold onto which neural trajectories unfold. From this perspective, a key property is the neural manifold’ intrinsic dimension (ID), which encapsulates the relation between individual units and collective activity, and hence the overall complexity of the neural code. However, characterizing the ID is far from trivial: linear dimension estimators (such as PCA and its variants) are unreliable in the presence of significant nonlinear correlations, while nonlinear ones are often fragile, demanding unrealistically large samples to yield accurate estimates.
The literature has thoroughly addressed ID estimation in small- and medium-scale recordings under simple tasks, where activity typically lies onto low-dimensional manifolds. Large-scale recordings (including thousands of units) have been comparatively understudied, but are of utmost importance given the recent surge in the availability of large-scale data sets. Focusing on second-order statistics, recent studies have argued that large-scale neural activity can be very high-dimensional, but this conclusion is undermined by the exclusive usage of a linear analysis approach.
Here, we consider several large-scale recordings from mice and zebrafish, including both spontaneous and visual-stimulation-evoked activity, and use an array of linear and nonlinear ID estimators to probe the geometry of the respective neural manifolds. Large-scale neural data pose significant technical challenges to the employed estimators, of which we thoroughly characterize the relative strengths and weaknesses.
Overall, our analysis indicates that neural trajectories lie onto high-dimensional, approximately linear manifolds, supporting and corroborating the conclusions of previous studies. Moreover, we show under which conditions the ID of the neural manifold could be reliably estimated by using a relatively low number of neurons and temporal samples.
| Preferred Presentation | Oral Presentation |
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