Inference on the Macroscopic Dynamics of Spiking Neurons

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Computation Pub Date : 2024-09-17 DOI:10.1162/neco_a_01701
Nina Baldy;Martin Breyton;Marmaduke M. Woodman;Viktor K. Jirsa;Meysam Hashemi
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Abstract

The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying the interactions of neurons. Estimating parameters of this class of generative model allows one to predict the system’s dynamics and responses under changing inputs and, indeed, changing parameters. We first assume a set of known state-space equations and address the problem of inferring the lumped parameters from observed time series. Crucially, we consider this problem in the setting of bistability, random fluctuations in system dynamics, and partial observations, in which some states are hidden. To identify the most efficient estimation or inversion scheme in this particular system identification, we benchmark against state-of-the-art optimization and Bayesian estimation algorithms, highlighting their strengths and weaknesses. Additionally, we explore how well the statistical relationships between parameters are maintained across different scales. We found that deep neural density estimators outperform other algorithms in the inversion scheme, despite potentially resulting in overestimated uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. We then eschew the mean-field approximation and employ deep neural ODEs on spiking neurons, illustrating prediction of system dynamics and vector fields from microscopic states. Overall, this study affords an opportunity to predict brain dynamics and responses to various perturbations or pharmacological interventions using deep neural networks.
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尖峰神经元宏观动态推论
对尖峰神经元网络的推理过程对于破译大脑计算和功能的内在机制至关重要。在这项研究中,我们简化了神经元的相互作用,对均值场近似模型的参数和动态进行推断。估算这类生成模型的参数可以预测系统在输入变化以及参数变化情况下的动态和反应。我们首先假设一组已知的状态空间方程,并解决从观测到的时间序列中推断集合参数的问题。最重要的是,我们是在双稳态、系统动态随机波动和部分观测(其中某些状态是隐藏的)的背景下考虑这个问题的。为了确定这一特定系统识别中最有效的估计或反演方案,我们以最先进的优化和贝叶斯估计算法为基准,突出它们的优缺点。此外,我们还探讨了参数之间的统计关系在不同尺度上的保持情况。我们发现,深度神经密度估计在反演方案中优于其他算法,尽管可能会导致参数之间的不确定性和相关性被高估。不过,这个问题可以通过时延嵌入得到改善。然后,我们摒弃了均场近似,在尖峰神经元上采用了深度神经 ODE,说明了从微观状态预测系统动态和向量场的方法。总之,这项研究为利用深度神经网络预测大脑动态和对各种扰动或药物干预的反应提供了机会。
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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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