AI-driven modelling for cortex-wide neuromodulated learning

动物和人类都可以从感觉输入中学习认知任务。然而，现有的计算框架在学习这类任务方面极其缓慢。因此，迫切需要能够快速学习认知任务的新一代计算框架。在这里，我们认为神经调节和特定的兴奋-抑制电路一起使多个大脑区域能够有效地学习。基于我们在开发生物学上可信的人工智能驱动的学习计算模型方面的专业知识，我们将开发一个神经调节的皮质范围学习的自适应突触-行为模型。首先，我们将展示对细胞类型的精确神经调制控制导致认知任务的全脑快速学习，但也会产生对扰动更健壮的神经网络。接下来，将使用现有的实验数据来检验该模型产生的预测。我们将在细胞、系统和行为层面测试该模型。总体而言，拟议的集成人工智能驱动的计算框架将对于我们理解健康和疾病中认知任务的皮质范围学习至关重要。

Both animals and humans can learn cognitive tasks from sensory inputs. However, existing computational frameworks are extremely slow at learning such tasks. Therefore, a new generation of computational frameworks capable of rapid learning of cognitive tasks is urgently needed. Here we propose that neuromodulation together with specific excitatory-inhibitory circuits enable efficient learning across multiple brain areas. Building on our expertise in developing biologically plausible AI-driven computational models of learning we will develop an adaptive synapse-to-behaviour model of neuromodulated cortex-wide learning. First, we will show that a precise neuromodulatory control of cell-types results in rapid brain-wide learning of cognitive tasks, but also in neural networks that are more robust to perturbations. Next, the predictions generated by the model will be tested using existing experimental data. We will test the model at the cellular, systems and behavioural level. Overall, the proposed integrative AI-driven computational framework, will be critical for our understanding of cortex-wide learning of cognitive tasks in both health and disease.