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研究揭示斑马鱼决策的神经回路
作者:小柯机器人 发布时间:2019/12/4 9:41:26

美国哈佛大学Armin Bahl组发现斑马鱼幼体证据积累和决策的神经回路。该项研究成果在线发表在2019年12月2日的《自然—神经科学》上。

研究人员通过将用于灵长类动物研究的随机点运动判别范式,应用于幼体斑马鱼来解决问题。使用斑马鱼的先天性光动力应答作为决策的一种度量,他们发现幼体斑马鱼会在数秒内积累并记住运动痕迹,并且其行为与有限泄漏积分器模型非常一致。通过使用全脑功能成像,他们确定了前后脑中的三个神经元簇,使它们非常适合执行基础计算。通过将这些结构内的动力学与个体行为选择相关联,他们提出了一种生物物理上合理的电路分布,其中证据集成器与动态决策阈值竞争以激活下游电机命令。

据介绍,为了做出正确的决定,动物需要积累感觉证据。简单的积分器模型可以解释许多方面的这种行为,但是如何在大脑中以机械方式实现基础计算仍知之甚少。

附:英文原文

Title: Neural circuits for evidence accumulation and decision making in larval zebrafish

Author: Armin Bahl, Florian Engert

Issue&Volume: 2019-12-02

Abstract: To make appropriate decisions, animals need to accumulate sensory evidence. Simple integrator models can explain many aspects of such behavior, but how the underlying computations are mechanistically implemented in the brain remains poorly understood. Here we approach this problem by adapting the random-dot motion discrimination paradigm, classically used in primate studies, to larval zebrafish. Using their innate optomotor response as a measure of decision making, we find that larval zebrafish accumulate and remember motion evidence over many seconds and that the behavior is in close agreement with a bounded leaky integrator model. Through the use of brain-wide functional imaging, we identify three neuronal clusters in the anterior hindbrain that are well suited to execute the underlying computations. By relating the dynamics within these structures to individual behavioral choices, we propose a biophysically plausible circuit arrangement in which an evidence integrator competes against a dynamic decision threshold to activate a downstream motor command.

DOI: 10.1038/s41593-019-0534-9

Source: https://www.nature.com/articles/s41593-019-0534-9

期刊信息

Nature Neuroscience:《自然—神经科学》,创刊于1998年。隶属于施普林格·自然出版集团,最新if:21.126
官方网址:https://www.nature.com/neuro/
投稿链接:https://mts-nn.nature.com/cgi-bin/main.plex