P.133 Neurons in the lateral prefrontal cortex encode task features during virtual navigation

M. Abbass, B. Corrigan, R. Johnston, R. Gulli, A. Sachs, JC Lau, J. Martinez-Trujillo
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Abstract

Background: The lateral prefrontal cortex (LPFC) is uniquely found in primates and has been associated with contextual learning. This function is thought to be subserved by neurons that are tuned to abstract concepts and the combination of those concepts. LPFC neuron tuning remains to be fully investigated in naturalistic conditions. Methods: Two macaques were trained to perform a context-colour association task while using a joystick to navigate in an X-shaped maze. They were implanted with two 96-channel microelectrode arrays, targeting the LPFC. Mean firing rates were computed and multivariate linear regressions were used to determine tuning. Results: LPFC neurons were tuned to context (12.4%), color position (6.2%), target side (17.2%), and were selective to more than one feature (21.2%). LPFC neurons acquired tuning to task features in an ordered manner, starting with context (130.1±27.4ms), followed by the colour position (296.2±21.4ms) and then target side (493.3±19.3ms). Furthermore, most neurons (54%) changed their tuning over time. Conclusions: We demonstrate that single neurons can encode relevant features embedded in a naturalistic virtual environment. Our results support previous observations that LPFC neurons combine individual features and suggest that these features are also combined temporally. These findings contribute towards understanding the LPFC and have potential practical implications.
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P.133 外侧前额叶皮层神经元在虚拟导航过程中编码任务特征
背景:外侧前额叶皮层(LPFC)是灵长类动物特有的皮层,与情境学习有关。这种功能被认为是由神经元提供的,这些神经元对抽象概念和这些概念的组合进行调谐。LPFC神经元调谐仍有待在自然条件下进行全面研究。研究方法对两只猕猴进行训练,让它们在使用操纵杆在X形迷宫中导航的同时完成情境-颜色联想任务。它们被植入了两个 96 通道的微电极阵列,目标是 LPFC。计算平均发射率并使用多元线性回归确定调谐。结果LPFC神经元对上下文(12.4%)、颜色位置(6.2%)、目标侧面(17.2%)进行调谐,并对一种以上的特征具有选择性(21.2%)。LPFC 神经元对任务特征的调谐是有序的,首先是上下文(130.1±27.4ms),其次是颜色位置(296.2±21.4ms),然后是目标侧面(493.3±19.3ms)。此外,大多数神经元(54%)的调谐随时间而改变。结论我们证明了单个神经元可以编码自然虚拟环境中的相关特征。我们的研究结果支持了之前的观察结果,即 LPFC 神经元结合了单个特征,并表明这些特征也是在时间上结合的。这些发现有助于理解 LPFC,并具有潜在的实际意义。
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