The emergence of cross-modal learning capabilities requires the interaction of neural areas accounting for sensory and cognitive processing. Convergence of multiple sensory inputs is observed in low-level sensory cortices including primary somatosensory (S1), visual (V1), and auditory cortex (A1), as well as in high-level areas such as prefrontal cortex (PFC). Evidence shows that local neural activity and functional connectivity between sensory cortices participate in cross-modal processing. However, little is known about the functional interplay between neural areas underlying sensory and cognitive processing required for cross-modal learning capabilities across life. Here we review our current knowledge on the interdependence of low- and high-level cortices for the emergence of cross-modal processing in rodents. First, we summarize the mechanisms underlying the integration of multiple senses and how cross-modal processing in primary sensory cortices might be modified by top-down modulation of the PFC. Second, we examine the critical factors and developmental mechanisms that account for the interaction between neuronal networks involved in sensory and cognitive processing. Finally, we discuss the applicability and relevance of cross-modal processing for brain-inspired intelligent robotics. An in-depth understanding of the factors and mechanisms controlling cross-modal processing might inspire the refinement of robotic systems by better mimicking neural computations.

中文翻译：

---

低水平感官和高级认知过程的对话：大脑的跨模态能力的发展，机制和相关性。

跨模式学习能力的出现要求考虑感觉和认知过程的神经区域的相互作用。在包括初级体感（S1），视觉（V1）和听觉皮层（A1）以及高级区域（如前额叶皮层（PFC））的低层感觉皮层中观察到多个感觉输入的收敛。有证据表明，感觉神经皮层之间的局部神经活动和功能连接参与了交叉模态处理。然而，关于跨生命的跨模式学习能力所需的感觉和认知处理的神经区域之间的功能相互作用知之甚少。在这里，我们回顾我们对低水平和高水平皮质的相互依存性的认识，以了解啮齿类动物中的交叉模式加工的出现。第一，我们总结了多种感官整合的潜在机制，以及如何通过自上而下的PFC调制来修改初级感觉皮层中的交叉模式处理。第二，我们研究了影响感觉和认知过程的神经元网络之间相互作用的关键因素和发展机制。最后，我们讨论了跨模式处理在脑启发式智能机器人中的适用性和相关性。对控制交叉模式处理的因素和机制的深入了解可能会通过更好地模仿神经计算来激发机器人系统的完善。我们研究了影响感觉和认知过程的神经元网络之间相互作用的关键因素和发展机制。最后，我们讨论了跨模式处理在脑启发式智能机器人中的适用性和相关性。对控制交叉模式处理的因素和机制的深入了解可能会通过更好地模仿神经计算来激发机器人系统的完善。我们研究了影响感觉和认知过程的神经元网络之间相互作用的关键因素和发展机制。最后，我们讨论了跨模式处理在脑启发式智能机器人中的适用性和相关性。对控制交叉模式处理的因素和机制的深入了解可能会通过更好地模仿神经计算来激发机器人系统的完善。