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.

学习取决于行为反馈。在大脑中，这种反馈是由弥漫性神经调节剂编码的。据信，这些神经调节剂可以通过将信用分配给整个皮层的突触来触发学习。但是，最近的理论发现表明，这种分散的信用分配导致学习非常缓慢，与动物和人类学习形成鲜明对比。这提出了一个基本问题：如何使用弥漫性神经调节有效地学习皮层？在我在开发皮质学习计算模型方面的专业知识的基础上，我们将开发一个神经调节的皮质信用分配的综合性突触计算模型。首先，我们将证明兴奋性抑制性皮质细胞类型的多巴胺能神经调节可以快速基于奖励的信用分配。接下来，利用自适应机器学习原则，我建议胆碱能神经调节与多巴胺能系统合作，通过民主调节兴奋性抑制性皮质电路来促进和稳健学习。为了确保该模型在生物学和功能上保持合理，它将与最新的实验数据与领先的实验者和机器学习研究人员密切合作形成鲜明对比。在本文中，这种多巴胺能 - 胆碱能模型将对以下问题产生可测试的预测：（i）兴奋性抑制性抑制性抑制性细胞型的作用是什么？ （ii）对于快速和强大的目标驱动学习，神经调节必须多具体？ （iii）为什么神经调节性故障通常与痴呆症和衰老的认知能力下降相关？总的来说，提出的整合计算框架，对于我们对健康和疾病中的皮质广泛行为息息相关至关重要。