Intrinsic dimensionality analysis of neuronal population activity

The manifold hypothesis in application to neural data states that brain activity patterns are confined to a low-dimensional manifold that spans a small fraction of the theoretically available state space. We test this hypothesis using in vivo calcium imaging data from hippocampal neurons in mice during novel arena exploration. We compute the intrinsic dimensionality of neural activity and compare it with other dimensionality estimates. Due to the inevitable scarcity of in vivo recordings, both in terms of neurons recorded and duration, we follow the dimensionality patterns as the amount of available data increases. We find a nontrivial power-law scaling of intrinsic dimension with the number of recorded neurons, and discuss its potential biological significance.

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Published
2025-08-25
DOI
https://doi.org/10.1109/cnn67635.2025.11177525
Primary Topic
Neural dynamics and brain function
Type
article
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article

Intrinsic dimensionality analysis of neuronal population activity

В. А. Аветисов, K. V. Anokhin, Nikita Pospelov, О. И. Ивашкина et al.
Neural dynamics and brain function
article

Intrinsic dimensionality analysis of neuronal population activity

В. А. Аветисов, K. V. Anokhin, Nikita Pospelov, О. И. Ивашкина, Viktor Plusnin, К. А. Торопова, Olga Rogozhnikova
article en

Abstract

The manifold hypothesis in application to neural data states that brain activity patterns are confined to a low-dimensional manifold that spans a small fraction of the theoretically available state space. We test this hypothesis using in vivo calcium imaging data from hippocampal neurons in mice during novel arena exploration. We compute the intrinsic dimensionality of neural activity and compare it with other dimensionality estimates. Due to the inevitable scarcity of in vivo recordings, both in terms of neurons recorded and duration, we follow the dimensionality patterns as the amount of available data increases. We find a nontrivial power-law scaling of intrinsic dimension with the number of recorded neurons, and discuss its potential biological significance.

Lomonosov Moscow State University (RU), Semenov Institute of Chemical Physics (RU)
Openalex Percentile: Top 73%
Neural dynamics and brain function
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