TY - JOUR

T1 - Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity

AU - Kawahara, Daisuke

AU - Fujisawa, Shigeyoshi

N1 - Publisher Copyright:
© 2024 Massachusetts Institute of Technology.

PY - 2024/3

Y1 - 2024/3

N2 - 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.

AB - 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.

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U2 - 10.1162/neco_a_01645

DO - 10.1162/neco_a_01645

M3 - Letter

C2 - 38363660

AN - SCOPUS:85185410649

SN - 0899-7667

VL - 36

SP - 385

EP - 411

JO - Neural Computation

JF - Neural Computation

IS - 3

ER -