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

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Computation Pub Date : 2024-02-16 DOI:10.1162/neco_a_01645
Daisuke Kawahara;Shigeyoshi Fujisawa
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Abstract

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.
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从网格细胞种群活动推测动物位置的持续同源性优势
许多认知功能都是以细胞群的形式表现出来的。就空间导航而言,海马体中的位置细胞和内侧皮层中的网格细胞的群体活动代表了环境中的自我定位。大脑无法直接观察环境中的自我定位信息。相反,它依靠感觉信息和记忆来估计自我定位。因此,仅从高维神经活动中估算低维动态,如动物探索环境的运动轨迹,对于解读大脑所代表的信息非常重要。以往的大多数研究都是通过使用人工神经网络或高斯过程进行贝叶斯群体解码的无监督学习来估计神经活动背后的低维动态(即潜在变量)。最近,持续共生被用于从神经活动所创建流形的相位信息(即圆坐标)中估计潜变量。然而,与贝叶斯群体解码法相比,持久同调法的优势尚不十分明确。我们比较了持续同构和贝叶斯种群解码在从模拟和实际网格细胞种群活动中估计动物位置方面的优势。我们发现,与贝叶斯种群解码相比,持久同调法能以更少的神经元估计动物位置,并能从实际的噪声数据中稳健地估计动物位置。
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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