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Nonlinear manifolds underlie neural population activity during behaviour.

Cátia Fortunato¹, Jorge Bennasar-Vázquez¹, Junchol Park²

¹Department of Bioengineering, Imperial College London, London UK.

Biorxiv : the Preprint Server for Biology

|July 28, 2023

Summary

Neural population activity, previously modeled as flat, is intrinsically nonlinear. This nonlinearity, crucial for complex behaviors, varies across brain regions and is influenced by neural circuit connectivity.

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Area of Science:

Neuroscience
Computational Neuroscience
Systems Neuroscience

Background:

Single neuron activity during behavior exhibits high diversity.
Neural population activity is often simplified using low-dimensional models, assuming linear relationships.
Existing models constrain neural population activity to a flat "neural manifold".

Approach:

Investigated the intrinsic dimensionality of neural manifolds using population recordings from monkey, mouse, and human motor cortex, and mouse striatum.
Utilized recurrent neural network (RNN) models to simulate and confirm the relationship between circuit connectivity and manifold nonlinearity.
Analyzed how manifold nonlinearity changes during tasks requiring varied activity patterns and across different brain regions.

Key Points:

Neural manifolds are intrinsically nonlinear, not flat as previously assumed.
Manifold nonlinearity increases with task complexity, reflecting more diverse neural activity.
Nonlinearity differs across architecturally distinct brain regions, suggesting region-specific circuit properties.

Conclusions:

Neural manifolds underlying behavior generation are inherently nonlinear.
Accounting for nonlinearity is essential for understanding complex, naturalistic behaviors.
Circuit connectivity plays a key role in shaping manifold nonlinearity across brain regions.