AI-driven modelling for cortex-wide neuromodulated learning

点击翻译按钮获取中文摘要

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.