Adeel Ahsan
Oxford Computational Neuroscience, Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom.
School of Mathematics and Computer Science, University of Wolverhampton, Wolverhampton, United Kingdom.
Front Comput Neurosci. 2020 May 19;14:15. doi: 10.3389/fncom.2020.00015. eCollection 2020.
Conscious awareness plays a major role in human cognition and adaptive behavior, though its function in multisensory integration is not yet fully understood, hence, questions remain: How does the brain integrate the incoming multisensory signals with respect to different external environments? How are the roles of these multisensory signals defined to adhere to the anticipated behavioral-constraint of the environment? This work seeks to articulate a novel theory on conscious multisensory integration (CMI) that addresses the aforementioned research challenges. Specifically, the well-established contextual field (CF) in pyramidal cells and coherent infomax theory (Kay et al., 1998; Kay and Phillips, 2011) is split into two functionally distinctive integrated input fields: local contextual field (LCF) and universal contextual field (UCF). LCF defines the modulatory sensory signal coming from some other parts of the brain (in principle from anywhere in space-time) and UCF defines the outside environment and anticipated behavior (based on past learning and reasoning). Both LCF and UCF are integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN), which adapts to changing environments. The proposed theory is evaluated using human contextual audio-visual (AV) speech modeling. Simulation results provide new insights into contextual modulation and selective multisensory information amplification/suppression. The central hypothesis reviewed here suggests that the pyramidal cell, in addition to the classical excitatory and inhibitory signals, receives LCF and UCF inputs. The UCF (as a steering force or tuner) plays a decisive role in precisely selecting whether to amplify/suppress the transmission of relevant/irrelevant feedforward signals, without changing the content e.g., which information is worth paying more attention to? This, as opposed to, unconditional excitatory and inhibitory activity in existing deep neural networks (DNNs), is called conditional amplification/suppression.
意识觉知在人类认知和适应性行为中起着重要作用，尽管其在多感官整合中的功能尚未完全被理解，因此，问题依然存在：大脑如何针对不同的外部环境整合传入的多感官信号？这些多感官信号的作用是如何被定义以符合环境预期的行为约束的？这项工作旨在阐明一种关于意识多感官整合（CMI）的新理论，以应对上述研究挑战。具体而言，将锥体细胞中成熟的上下文场（CF）和相干信息最大化理论（Kay等人，1998年；Kay和Phillips，2011年）分为两个功能不同的整合输入场：局部上下文场（LCF）和通用上下文场（UCF）。LCF定义来自大脑其他部分（原则上来自时空的任何地方）的调制感官信号，UCF定义外部环境和预期行为（基于过去的学习和推理）。LCF和UCF都与感受野（RF）整合，以开发一类新的上下文自适应神经元（CAN），其可适应不断变化的环境。使用人类上下文视听（AV）语音建模对所提出的理论进行评估。模拟结果为上下文调制和选择性多感官信息放大/抑制提供了新的见解。这里回顾的核心假设表明，锥体细胞除了接收经典的兴奋性和抑制性信号外，还接收LCF和UCF输入。UCF（作为一种引导力或调谐器）在精确选择是否放大/抑制相关/不相关前馈信号的传输方面起着决定性作用，而不改变内容，例如，哪些信息值得更多关注？与现有深度神经网络（DNN）中的无条件兴奋性和抑制性活动相反，这被称为条件放大/抑制。
