计算神经科学：数学与统计视角
Computational Neuroscience: Mathematical and Statistical Perspectives.
作者信息
Kass Robert E, Amari Shun-Ichi, Arai Kensuke, Brown Emery N, Diekman Casey O, Diesmann Markus, Doiron Brent, Eden Uri T, Fairhall Adrienne L, Fiddyment Grant M, Fukai Tomoki, Grün Sonja, Harrison Matthew T, Helias Moritz, Nakahara Hiroyuki, Teramae Jun-Nosuke, Thomas Peter J, Reimers Mark, Rodu Jordan, Rotstein Horacio G, Shea-Brown Eric, Shimazaki Hideaki, Shinomoto Shigeru, Yu Byron M, Kramer Mark A
机构信息
Carnegie Mellon University, Pittsburgh, PA, USA, 15213; email:
RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198.
出版信息
Annu Rev Stat Appl. 2018 Mar;5:183-214. doi: 10.1146/annurev-statistics-041715-033733. Epub 2017 Dec 8.
Abstract
Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
摘要
数学和统计模型在神经科学中发挥了重要作用，特别是通过描述单个记录的神经元或跨大型网络集体记录的神经元的电活动。随着该领域迅速发展，新的挑战也在出现。为了实现最大的有效性，致力于推进计算神经科学的人员需要认识并利用机械理论和统计范式的互补优势。
相似文献
1
Computational Neuroscience: Mathematical and Statistical Perspectives.计算神经科学：数学与统计视角
Annu Rev Stat Appl. 2018 Mar;5:183-214. doi: 10.1146/annurev-statistics-041715-033733. Epub 2017 Dec 8.
2
Training deep neural density estimators to identify mechanistic models of neural dynamics.训练深度神经网络密度估计器以识别神经动力学的机制模型。
Elife. 2020 Sep 17;9:e56261. doi: 10.7554/eLife.56261.
3
Likelihood approximation networks (LANs) for fast inference of simulation models in cognitive neuroscience.用于认知神经科学中模拟模型快速推断的似然逼近网络 (LANs)。
Elife. 2021 Apr 6;10:e65074. doi: 10.7554/eLife.65074.
4
Mathematical studies of the dynamics of finite-size binary neural networks: A review of recent progress.有限尺寸二进制神经网络动力学的数学研究：近期进展综述。
Math Biosci Eng. 2019 Sep 4;16(6):8025-8059. doi: 10.3934/mbe.2019404.
5
Neural Activity in Quarks Language: Lattice Field Theory for a Network of Real Neurons.夸克语言中的神经活动：真实神经元网络的格点场论
Entropy (Basel). 2024 Jun 6;26(6):495. doi: 10.3390/e26060495.
6
Model-based cognitive neuroscience.基于模型的认知神经科学。
J Math Psychol. 2017 Feb;76(Pt B):59-64. doi: 10.1016/j.jmp.2016.10.010. Epub 2016 Nov 23.
7
Cognitive network neuroscience.认知网络神经科学
J Cogn Neurosci. 2015 Aug;27(8):1471-91. doi: 10.1162/jocn_a_00810. Epub 2015 Mar 24.
8
Computational modeling of neural plasticity for self-organization of neural networks.神经网络自组织的神经可塑性计算建模。
Biosystems. 2014 Nov;125:43-54. doi: 10.1016/j.biosystems.2014.04.003. Epub 2014 Apr 24.
9
Neural Information Processing in Cognition: We Start to Understand the Orchestra, but Where is the Conductor?认知中的神经信息处理：我们开始了解管弦乐队，但指挥在哪里？
Front Comput Neurosci. 2016 Jan 26;10:3. doi: 10.3389/fncom.2016.00003. eCollection 2016.
10
Neural field models for latent state inference: Application to large-scale neuronal recordings.用于潜在状态推断的神经场模型：在大规模神经元记录中的应用。
PLoS Comput Biol. 2019 Nov 4;15(11):e1007442. doi: 10.1371/journal.pcbi.1007442. eCollection 2019 Nov.
引用本文的文献
1
Phase transitions in or populations of spiking neurons belong to different universality classes.尖峰神经元群体或其中的相变属于不同的普适类。
ArXiv. 2025 May 13:arXiv:2301.09600v4.
2
Depth-based statistical analysis in the spike train space.尖峰序列空间中基于深度的统计分析。
J Appl Stat. 2024 Jul 2;52(2):329-355. doi: 10.1080/02664763.2024.2369954. eCollection 2025.
3
Exploring the Frontiers of Mathematical Neuroscience: A Comprehensive Bibliometric Analysis.探索数学神经科学前沿：全面的文献计量分析
Cureus. 2024 Oct 10;16(10):e71213. doi: 10.7759/cureus.71213. eCollection 2024 Oct.
4
Automated customization of large-scale spiking network models to neuronal population activity.大规模尖峰网络模型到神经元群体活动的自动化定制。
Nat Comput Sci. 2024 Sep;4(9):690-705. doi: 10.1038/s43588-024-00688-3. Epub 2024 Sep 16.
5
Oscillating neural circuits: Phase, amplitude, and the complex normal distribution.振荡神经回路：相位、振幅与复正态分布。
Can J Stat. 2023 Sep;51(3):824-851. doi: 10.1002/cjs.11790. Epub 2023 Jul 22.
6
Neurobiological Causal Models of Language Processing.语言处理的神经生物学因果模型
Neurobiol Lang (Camb). 2024 Apr 1;5(1):225-247. doi: 10.1162/nol_a_00133. eCollection 2024.
7
Inference of network connectivity from temporally binned spike trains.从时间分箱的尖峰火车推断网络连通性。
J Neurosci Methods. 2024 Apr;404:110073. doi: 10.1016/j.jneumeth.2024.110073. Epub 2024 Feb 2.
8
Discovering Low-Dimensional Descriptions of Multineuronal Dependencies.发现多神经元依赖性的低维描述。
Entropy (Basel). 2023 Jul 6;25(7):1026. doi: 10.3390/e25071026.
9
Identification of interacting neural populations: methods and statistical considerations.鉴定相互作用的神经群体：方法与统计考虑。
J Neurophysiol. 2023 Sep 1;130(3):475-496. doi: 10.1152/jn.00131.2023. Epub 2023 Jul 19.
10
Biophysical Model: A Promising Method in the Study of the Mechanism of Propofol: A Narrative Review.生物物理模型：丙泊酚作用机制研究的一种有前途的方法：叙事性综述。
Comput Intell Neurosci. 2022 May 17;2022:8202869. doi: 10.1155/2022/8202869. eCollection 2022.
本文引用的文献
1
Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models.用广义线性模型捕捉单个神经元的动态特征库。
Neural Comput. 2017 Dec;29(12):3260-3289. doi: 10.1162/neco_a_01021. Epub 2017 Sep 28.
2
Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing.深度神经网络：一种用于模拟生物视觉和大脑信息处理的新框架。
Annu Rev Vis Sci. 2015 Nov 24;1:417-446. doi: 10.1146/annurev-vision-082114-035447.
3
Spike-Centered Jitter Can Mistake Temporal Structure.以尖峰为中心的抖动会误判时间结构。
Neural Comput. 2017 Mar;29(3):783-803. doi: 10.1162/NECO_a_00927. Epub 2017 Jan 17.
4
Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models.神经群体与网络模型降维的标度性质
PLoS Comput Biol. 2016 Dec 7;12(12):e1005141. doi: 10.1371/journal.pcbi.1005141. eCollection 2016 Dec.
5
Surrogate Data Methods Based on a Shuffling of the Trials for Synchrony Detection: The Centering Issue.基于试验洗牌的同步检测替代数据方法：中心化问题。
Neural Comput. 2016 Nov;28(11):2352-2392. doi: 10.1162/NECO_a_00839.
6
Identifying Anatomical Origins of Coexisting Oscillations in the Cortical Microcircuit.识别皮质微回路中共存振荡的解剖学起源。
PLoS Comput Biol. 2016 Oct 13;12(10):e1005132. doi: 10.1371/journal.pcbi.1005132. eCollection 2016 Oct.
7
Small-World Brain Networks Revisited.再次探讨小世界脑网络。
Neuroscientist. 2017 Oct;23(5):499-516. doi: 10.1177/1073858416667720. Epub 2016 Sep 21.
8
Synchronous Spike Patterns in Macaque Motor Cortex during an Instructed-Delay Reach-to-Grasp Task.在指令延迟伸手抓握任务期间猕猴运动皮层中的同步尖峰模式
J Neurosci. 2016 Aug 10;36(32):8329-40. doi: 10.1523/JNEUROSCI.4375-15.2016.
9
Analysis of Neuronal Spike Trains, Deconstructed.神经元脉冲序列分析，解构剖析。
Neuron. 2016 Jul 20;91(2):221-59. doi: 10.1016/j.neuron.2016.05.039.
10
Beware of the Small-World Neuroscientist!小心这位“小世界”神经科学家！
Front Hum Neurosci. 2016 Mar 8;10:96. doi: 10.3389/fnhum.2016.00096. eCollection 2016.
Zotero 插件