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一个整合了线索恐惧条件反射、消退和恢复中多种现象的计算模型。

A Computational Model Integrating Multiple Phenomena on Cued Fear Conditioning, Extinction, and Reinstatement.

作者信息

Mattera Andrea, Pagani Marco, Baldassarre Gianluca

机构信息

Institute of Cognitive Sciences and Technologies, National Research Council, Rome, Italy.

出版信息

Front Syst Neurosci. 2020 Sep 29;14:569108. doi: 10.3389/fnsys.2020.569108. eCollection 2020.


DOI:10.3389/fnsys.2020.569108
PMID:33132856
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7550679/
Abstract

Conditioning, extinction, and reinstatement are fundamental learning processes of animal adaptation, also strongly involved in human pathologies such as post-traumatic stress disorder, anxiety, depression, and dependencies. Cued fear conditioning, extinction, restatement, and systematic manipulations of the underlying brain amygdala and medial prefrontal cortex, represent key experimental paradigms to study such processes. Numerous empirical studies have revealed several aspects and the neural systems and plasticity underlying them, but at the moment we lack a comprehensive view. Here we propose a computational model based on firing rate leaky units that contributes to such integration by accounting for 25 different experiments on fear conditioning, extinction, and restatement, on the basis of a single neural architecture having a structure and plasticity grounded in known brain biology. This allows the model to furnish three novel contributions to understand these open issues: (a) the functioning of the central and lateral amygdala system supporting conditioning; (b) the role played by the endocannabinoids system in within- and between-session extinction; (c) the formation of three important types of neurons underlying fear processing, namely fear, extinction, and persistent neurons. The model integration of the results on fear conditioning goes substantially beyond what was done in previous models.

摘要

条件作用、消退和恢复是动物适应的基本学习过程,在创伤后应激障碍、焦虑症、抑郁症和成瘾等人类病理状况中也起着重要作用。线索恐惧条件作用、消退、恢复以及对潜在脑杏仁核和内侧前额叶皮层的系统性操纵,是研究这些过程的关键实验范式。大量实证研究揭示了这些过程的几个方面以及其背后的神经系统和可塑性,但目前我们缺乏一个全面的观点。在此,我们提出一种基于发放率泄漏单元的计算模型,该模型基于具有已知脑生物学基础结构和可塑性的单一神经架构,通过考虑25个关于恐惧条件作用、消退和恢复的不同实验,为这种整合做出了贡献。这使得该模型能够为理解这些未解决问题提供三个新的贡献:(a)支持条件作用的中央和外侧杏仁核系统的功能;(b)内源性大麻素系统在会话内和会话间消退中的作用;(c)恐惧处理背后的三种重要类型神经元的形成,即恐惧神经元、消退神经元和持续神经元。该模型对恐惧条件作用结果的整合大大超越了以往模型所做的工作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/7be9a5d6ab63/fnsys-14-569108-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/57b073ccd260/fnsys-14-569108-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/516858d8f6ae/fnsys-14-569108-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/ded651d62a5c/fnsys-14-569108-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/3c87c1de9ae5/fnsys-14-569108-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/c9bcb53b2d6c/fnsys-14-569108-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/8c67b018616f/fnsys-14-569108-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/d437e52b17d6/fnsys-14-569108-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/f8827988ae8e/fnsys-14-569108-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/05bd93cc8dee/fnsys-14-569108-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/36c1dd80e61e/fnsys-14-569108-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/06b73a2165c4/fnsys-14-569108-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/7be9a5d6ab63/fnsys-14-569108-g0012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/57b073ccd260/fnsys-14-569108-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/516858d8f6ae/fnsys-14-569108-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/ded651d62a5c/fnsys-14-569108-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/3c87c1de9ae5/fnsys-14-569108-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/c9bcb53b2d6c/fnsys-14-569108-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/8c67b018616f/fnsys-14-569108-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/d437e52b17d6/fnsys-14-569108-g0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/f8827988ae8e/fnsys-14-569108-g0008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/05bd93cc8dee/fnsys-14-569108-g0009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/36c1dd80e61e/fnsys-14-569108-g0010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/06b73a2165c4/fnsys-14-569108-g0011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fb7d/7550679/7be9a5d6ab63/fnsys-14-569108-g0012.jpg

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引用本文的文献

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[6]
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本文引用的文献

[1]
Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns.

Basic Clin Neurosci. 2020

[2]
Prefrontal somatostatin interneurons encode fear memory.

Nat Neurosci. 2019-12-16

[3]
A model of amygdala function following plastic changes at specific synapses during extinction.

Neurobiol Stress. 2019-4-1

[4]
Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models.

Front Comput Neurosci. 2018-7-3

[5]
Vasoactive Intestinal Polypeptide-Immunoreactive Interneurons within Circuits of the Mouse Basolateral Amygdala.

J Neurosci. 2018-6-28

[6]
Excitatory connections between the prelimbic and infralimbic medial prefrontal cortex show a role for the prelimbic cortex in fear extinction.

Nat Neurosci. 2018-4-23

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Biologically based neural circuit modelling for the study of fear learning and extinction.

NPJ Sci Learn. 2016

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Fear extinction requires infralimbic cortex projections to the basolateral amygdala.

Transl Psychiatry. 2018-3-6

[9]
GABAergic interneurons: The orchestra or the conductor in fear learning and memory?

Brain Res Bull. 2017-12-2

[10]
The central amygdala controls learning in the lateral amygdala.

Nat Neurosci. 2017-12

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