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热偏好的动力学模型揭示了独立的兴奋和抑制性学习途径。

A dynamical model of thermal preference reveals independent excitatory and inhibitory learning pathways.

机构信息

Department of Physics, Emory University, Atlanta, GA 30322.

ANTIBODY Healthcare Communications, Toronto, ON M5J 2P1, Canada.

出版信息

Proc Natl Acad Sci U S A. 2023 Mar 28;120(13):e2215191120. doi: 10.1073/pnas.2215191120. Epub 2023 Mar 20.

Abstract

is capable of learning and remembering behaviorally relevant cues such as smells, tastes, and temperature. This is an example of associative learning, a process in which behavior is modified by making associations between various stimuli. Since the mathematical theory of conditioning does not account for some of its salient aspects, such as spontaneous recovery of extinguished associations, accurate modeling of behavior of real animals during conditioning has turned out difficult. Here, we do this in the context of the dynamics of the thermal preference of . We quantify thermotaxis in response to various conditioning temperatures, starvation durations, and genetic perturbations using a high-resolution microfluidic droplet assay. We model these data comprehensively, within a biologically interpretable, multi-modal framework. We find that the strength of the thermal preference is composed of two independent, genetically separable contributions and requires a model with at least four dynamical variables. One pathway positively associates the experienced temperature independently of food and the other negatively associates with the temperature when food is absent. The multidimensional structure of the association strength provides an explanation for the apparent classical temperature-food association of thermal preference and a number of longstanding questions in animal learning, including spontaneous recovery, asymmetric response to appetitive vs. aversive cues, latent inhibition, and generalization among similar cues.

摘要

它能够学习和记忆与行为相关的线索,如气味、味道和温度。这是一种联想学习的例子,即通过在各种刺激之间建立关联来改变行为。由于条件作用的数学理论不能解释其一些显著方面,例如已消除的关联的自发恢复,因此准确地模拟真实动物在条件作用过程中的行为变得困难。在这里,我们在 的热偏好动力学的背景下做到了这一点。我们使用高分辨率微流控液滴测定法,量化了对各种条件温度、饥饿持续时间和遗传扰动的热趋性。我们在一个具有生物学解释的、多模态的框架内全面地对这些数据进行建模。我们发现,热偏好的强度由两个独立的、遗传上可分离的贡献组成,并且需要至少四个动力学变量的模型。一条途径独立于食物积极地将体验到的温度与食物不存在时的温度相关联,另一条途径则与之相反。关联强度的多维结构为 热偏好的明显经典温度-食物关联以及动物学习中的许多长期存在的问题提供了一个解释,包括自发恢复、对奖赏性和惩罚性线索的不对称反应、潜伏抑制和类似线索之间的泛化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/66e8/10068832/b1de08cba19f/pnas.2215191120fig01.jpg

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