The complex neural activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured PFC responses within contexts and revealed equally performing mechanisms. One model implemented context-dependent recurrent dynamics and relied on transient input amplification; the other relied on subtle contextual modulations of the inputs, providing constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both models revealed properties of inputs and recurrent dynamics that were not apparent from qualitative descriptions of PFC responses. By revealing mechanisms that are quantitatively consistent with complex cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation.

中文翻译：

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通过前额叶皮层动力学的线性近似来推断依赖于上下文的计算


前额叶皮层 （PFC） 的复杂神经活动是认知过程的标志。这些丰富的动力学是如何出现并支持神经计算的，在很大程度上是未知的。在这里，我们通过将动力学模型拟合到行为猴子的 PFC 种群反应来推断感觉输入上下文依赖性整合的基础机制。一类实现由外部输入驱动的线性动力学的模型，在上下文中准确捕获了 PFC 响应，并揭示了性能相同的机制。一个模型实现了上下文相关的递归动力学并依赖于瞬态输入放大;另一个依赖于输入的微妙上下文调制，为解释灵活的 PFC 反应和行为所需的感觉区域的注意力效应提供了限制。这两个模型都揭示了 PFC 反应的定性描述中不明显的输入和循环动力学的特性。通过揭示与复杂皮层动力学在数量上一致的机制，我们的建模方法提供了一个原则性和通用的框架来将神经种群活动和计算联系起来。