AI-driven modelling for cortex-wide neuromodulated learning

用于全皮层神经调节学习的人工智能驱动建模

• 批准号: BB/X013340/1
• 负责人: Rui Ponte Costa
• 金额: $ 25.75万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX013340%2F1
• 关键词: AI; driven; modelling; cortex; wide

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.

动物和人类都可以从感官输入中学习认知任务。然而，现有的计算框架在学习这些任务时非常缓慢。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，我们提出，神经调节与特定的兴奋-抑制回路一起，使跨多个大脑区域的有效学习成为可能。基于我们在开发生物学上合理的人工智能驱动的学习计算模型方面的专业知识，我们将开发一种神经调节皮层全学习的自适应突触到行为模型。首先，我们将证明细胞类型的精确神经调节控制导致认知任务的快速全脑学习，而且神经网络对扰动更鲁棒。接下来，将使用现有的实验数据来测试模型生成的预测。我们将在细胞、系统和行为层面测试该模型。总的来说，所提出的综合AI驱动的计算框架对于我们理解健康和疾病中认知任务的皮层范围学习至关重要。