Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity

Daisuke Kawahara, Shigeyoshi Fujisawa

研究成果: Letter査読

抄録

Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hip-pocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its en-vironment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent vari-ables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian pro-cesses. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data.

本文言語English
ページ(範囲)385-411
ページ数27
ジャーナルNeural Computation
36
3
DOI
出版ステータスPublished - 2024 3月

ASJC Scopus subject areas

  • 人文科学(その他)
  • 認知神経科学

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