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Encoding of Reward and Decoding Movement from the Frontal Eye Field during Smooth Pursuit Eye Movements

机译:平滑追踪眼球运动过程中来自正面眼场的奖励和解码运动的编码

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摘要

Expectation of reward potentiates sensorimotor transformations to drive vigorous movements. One of the main challenges in studying reward is to determine how representations of reward interact with the computations that drive behavior. We recorded activity in smooth pursuit neurons in the frontal eye field (FEF) of two male rhesus monkeys while controlling the eye speed by manipulating either reward size or target speed. The neurons encoded the different reward conditions more strongly than the different target speed conditions. This pattern could not be explained by differences in the eye speed, since the eye speed sensitivity of the neurons was also larger for the reward conditions. Pooling the responses by the preferred direction of the neurons attenuated the reward modulation and led to a tighter association between neural activity and behavior. Therefore, a plausible decoder such as the population vector could explain how the FEF both drives behavior and encodes reward beyond behavior.>SIGNIFICANCE STATEMENT Motor areas combine sensory and reward information to drive movement. To disambiguate these sources, we manipulated the speed of smooth pursuit eye movements by controlling either the size of the reward or the speed of the visual motion signals. We found that the relationship between activity in frontal eye field and eye kinematics varied: the eye speed sensitivity was larger for the different reward conditions than for the different target speed conditions. Decoders that pooled signals by the preferred direction of the neurons attenuated the reward modulations. These decoders may indicate how reward can be both encoded beyond eye kinematics at the single neuron level and drive movement at the population level.
机译:对报酬的期望会增强感觉运动转换,从而推动剧烈运动。研究奖励的主要挑战之一是确定奖励的表示形式如何与驱动行为的计算进行交互。我们记录了两只雄性恒河猴的额叶视野(FEF)中平滑追随神经元的活动,同时通过操纵奖励大小或目标速度来控制眼睛速度。与不同的目标速度条件相比,神经元对不同的奖励条件编码的强度更高。这种模式无法用眼速的差异来解释,因为对于奖励条件,神经元的眼速敏感性也更大。通过神经元的首选方向合并响应会减弱奖励调制,并导致神经活动与行为之间的联系更加紧密。因此,诸如人口矢量之类的合理解码器可以解释FEF如何既驱动行为又编码行为以外的奖励。>意义声明运动区域结合了感官和奖励信息来驱动运动。为了消除这些来源的歧义,我们通过控制奖励的大小或视觉运动信号的速度来控制平滑追随眼睛运动的速度。我们发现额叶视野中的活动与眼睛运动学之间的关系各不相同:对于不同的奖励条件,眼速度灵敏度比对不同的目标速度条件要大。通过神经元的首选方向合并信号的解码器会减弱奖励调制。这些解码器可以指示如何既可以在单个神经元级别上编码超出眼睛运动学的奖励,又可以在人口级别上驱动运动。

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