Neuroscience Department, Institut Pasteur, 75015 Paris, France.
Laboratory of Computational and Quantitative Biology, Sorbonne Université, 75005 Paris, France.
Proc Natl Acad Sci U S A. 2022 Sep 27;119(39):e2201304119. doi: 10.1073/pnas.2201304119. Epub 2022 Sep 19.
Several neuronal mechanisms have been proposed to account for the formation of cognitive abilities through postnatal interactions with the physical and sociocultural environment. Here, we introduce a three-level computational model of information processing and acquisition of cognitive abilities. We propose minimal architectural requirements to build these levels, and how the parameters affect their performance and relationships. The first sensorimotor level handles local nonconscious processing, here during a visual classification task. The second level or cognitive level globally integrates the information from multiple local processors via long-ranged connections and synthesizes it in a global, but still nonconscious, manner. The third and cognitively highest level handles the information globally and consciously. It is based on the global neuronal workspace (GNW) theory and is referred to as the conscious level. We use the trace and delay conditioning tasks to, respectively, challenge the second and third levels. Results first highlight the necessity of epigenesis through the selection and stabilization of synapses at both local and global scales to allow the network to solve the first two tasks. At the global scale, dopamine appears necessary to properly provide credit assignment despite the temporal delay between perception and reward. At the third level, the presence of interneurons becomes necessary to maintain a self-sustained representation within the GNW in the absence of sensory input. Finally, while balanced spontaneous intrinsic activity facilitates epigenesis at both local and global scales, the balanced excitatory/inhibitory ratio increases performance. We discuss the plausibility of the model in both neurodevelopmental and artificial intelligence terms.
已经提出了几种神经元机制来解释通过与物理和社会文化环境的后天相互作用形成认知能力。在这里,我们引入了一个用于信息处理和认知能力获取的三级计算模型。我们提出了构建这些层级的最小架构要求,以及参数如何影响它们的性能和关系。第一个感觉运动层级处理局部非意识处理,这里是在进行视觉分类任务时。第二个或认知层级通过远程连接全局整合来自多个局部处理器的信息,并以全局但仍然是非意识的方式对其进行综合。第三个也是认知上最高级别的层级全局处理信息。它基于全局神经元工作空间(GNW)理论,被称为意识层级。我们使用痕迹和延迟条件任务分别挑战第二级和第三级。结果首先强调了通过在局部和全局尺度上选择和稳定突触来进行后天发生的必要性,以使网络能够解决前两个任务。在全局尺度上,尽管感知和奖励之间存在时间延迟,但多巴胺似乎是正确提供信用分配所必需的。在第三级,需要中间神经元来在没有感觉输入的情况下在 GNW 内维持自我维持的表示。最后,虽然平衡的自发内在活动有利于局部和全局尺度的后天发生,但平衡的兴奋/抑制比会提高性能。我们从神经发育和人工智能的角度讨论了模型的合理性。