首页|Researcher at University of Tokyo Has Published New Study Findings on Neural Computation (Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity)
Researcher at University of Tokyo Has Published New Study Findings on Neural Computation (Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on neural computation. According to news reporting from Chiba, Japan, by NewsRx journalists, research stated, “Many cognitive functions are represented as cell assemblies.” Our news reporters obtained a quote from the research from University of Tokyo: “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. There- fore, 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.” According to the news editors, the research concluded: “However, the advantages of persistent coho-mology 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.” For more information on this research see: Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity. Neural Computation, 2024,36(3). The publisher for Neural Computation is MIT Press.
University of TokyoChibaJapanAsiaComputationNeural Computation
NETL
NSTL
Mit Press
