دورية أكاديمية
Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity.
| العنوان: | Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity. |
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| المؤلفون: | Kawahara D; Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Chiba 277-8563, Japan.; Laboratory for Systems Neurophysiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan 8361405631@edu.k.u-tokyo.ac.jp., Fujisawa S; Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Chiba 277-8563, Japan.; Laboratory for Systems Neurophysiology, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan shigeyoshi.fujisawa@riken.jp. |
| المصدر: | Neural computation [Neural Comput] 2024 Feb 16; Vol. 36 (3), pp. 385-411. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: MIT Press Country of Publication: United States NLM ID: 9426182 Publication Model: Print Cited Medium: Internet ISSN: 1530-888X (Electronic) Linking ISSN: 08997667 NLM ISO Abbreviation: Neural Comput Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Cambridge, Mass. : MIT Press, c1989- |
| مواضيع طبية MeSH: | Grid Cells*, Animals ; Bayes Theorem ; Entorhinal Cortex/physiology ; Hippocampus/physiology ; Neurons/physiology ; Models, Neurological ; Space Perception/physiology |
| مستخلص: | Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus 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 environment, 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 variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. 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. (© 2024 Massachusetts Institute of Technology.) |
| تواريخ الأحداث: | Date Created: 20240216 Date Completed: 20240219 Latest Revision: 20240219 |
| رمز التحديث: | 20240219 |
| DOI: | 10.1162/neco_a_01645 |
| PMID: | 38363660 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 1530-888X |
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| DOI: | 10.1162/neco_a_01645 |