Dopaminergic-cholinergic neuromodulation for rapid and democratic cortex-wide learning

多巴胺能胆碱能神经调节用于快速和民主的皮质范围学习

• 批准号: EP/Y027841/1
• 负责人: Rui Ponte Costa
• 金额: $ 160.39万
• 依托单位: University of Oxford
• 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=EP%2FY027841%2F1
• 关键词: Dopaminergic; cholinergic; neuromodulation; rapid; democratic

Learning depends on behavioural feedback. In the brain this feedback is encoded by diffuse neuromodulators. These neuromodulators are believed to trigger learning by assigning credit to synapses throughout the cortex. However, recent theoretical findings show that such diffuse credit assignment leads to very slow learning, in sharp contrast with animal and human learning. This raises a fundamental question: how can the cortex learn efficiently using diffuse neuromodulation?\Here I propose that neuromodulators rely on excitatory-inhibitory circuits to assign credit efficiently to synapses throughout the cortex. Building on my expertise in developing computational models of cortical learning we will develop an integrative synapse-to-behaviour computational model of neuromodulated cortex- wide credit assignment. First, we will show that dopaminergic neuromodulation of excitatory-inhibitory cortical cell-types enables rapid reward-based credit assignment. Next, leveraging on adaptive machine learning principles I propose that cholinergic neuromodulation cooperates with the dopaminergic system to facilitate and robustify learning through democratic modulation of excitatory-inhibitory cortical circuits. To ensure that the model remains biologically and functionally sound, it will be contrasted with recent experimental data in close collaboration with leading experimentalists and machine learning researchers.Finally, this dopaminergic-cholinergic model will generate testable predictions to the following questions: (i) what is the role of excitatory-inhibitory cell-types during goal-driven learning? (ii) how specific must neuromodulation be for rapid and robust goal-driven learning? and (iii) why is neuromodulatory malfunction commonly associated with cognitive decline in dementia and aging?Overall, the proposed integrative computational framework, will be critical for our understanding of cortex- wide behaviourally-relevant learning in both health and disease.

学习依赖于行为反馈。在大脑中，这种反馈是由弥漫神经调节器编码的。这些神经调节剂被认为通过将信用分配给整个大脑皮层的突触来触发学习。然而，最近的理论发现表明，这种分散的学分分配导致学习非常缓慢，这与动物和人类的学习形成了鲜明对比。这就提出了一个基本问题：大脑皮层如何利用弥漫性神经调节有效地学习？在这里，我提出神经调节器依靠兴奋抑制回路将信用有效地分配给整个大脑皮层的突触。基于我在开发大脑皮层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整个大脑皮层信用分配的综合突触-行为计算模型。首先，我们将证明兴奋性抑制皮质细胞类型的多巴胺能神经调节能够实现基于奖励的快速信用分配。接下来，利用自适应机器学习原理，我提出胆碱能神经调节与多巴胺能系统合作，通过对兴奋抑制皮质回路的民主调制来促进和增强学习。为了确保该模型在生物学和功能上保持完好，将与最新的实验数据进行对比，与领先的实验学家和机器学习研究人员密切合作。最后，这个多巴胺能-胆碱能模型将对以下问题产生可检验的预测：(I)兴奋抑制细胞类型在目标驱动学习中的作用是什么？(Ii)对于快速和稳健的目标驱动学习，神经调节必须有多具体？以及(Iii)为什么神经调节功能障碍通常与痴呆症和衰老的认知能力下降有关？总体而言，拟议的综合计算框架将对我们理解健康和疾病中的大脑皮层行为相关学习至关重要。